Search ICLR 2019

Searching papers submitted to ICLR 2019 can be painful. You might want to know which paper uses technique X, dataset D, or cites author ME. Unfortunately, search is limited to titles, abstracts, and keywords, missing the actual contents of the paper. This Frankensteinian search has returned from 2018 to help scour the papers of ICLR by ripping out their souls using pdftotext.

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How about: distributional rl, variational autoencoders, structured scene representations, attention network, bayes-adaptive markov decision process ..?

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"normalization" has 606 results


tl;dr we proposed a new self-driving model which is composed of perception module for see and think and driving module for behave to acquire better generalization and accident explanation ability.

Current end-to-end deep learning driving models have two problems: (1) Poor generalization ability of unobserved driving environment when diversity of train- ing driving dataset is limited (2) Lack of accident explanation ability when driving models don’t work as expected. To tackle these two problems, rooted on the be- lieve that knowledge of associated easy task is benificial for addressing difficult task, we proposed a new driving model which is composed of perception module for see and think and driving module for behave, and trained it with multi-task perception-related basic knowledge and driving knowledge stepwisely. Specifi- cally segmentation map and depth map (pixel level understanding of images) were considered as what & where and how far knowledge for tackling easier driving- related perception problems before generating final control commands for difficult driving task. The results of experiments demonstrated the effectiveness of multi- task perception knowledge for better generalization and accident explanation abil- ity. With our method the average sucess rate of finishing most difficult navigation tasks in untrained city of CoRL test surpassed current benchmark method for 15 percent in trained weather and 20 percent in untrained weathers.

Improving Gaussian mixture latent variable model convergence with Optimal Transport    

tl;dr This paper shows that the Wasserstein distance objective enables the training of latent variable models with discrete latents in a case where the Variational Autoencoder objective fails to do so.

Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets. They present, however, subtleties in training often manifesting in the discrete latent variable not being leveraged. In this paper, we show why such models struggle to train using traditional log-likelihood maximization, and that they are amenable to training using the Optimal Transport framework of Wasserstein Autoencoders. We find our discrete latent variable to be fully leveraged by the model when trained, without any modifications to the objective function or significant fine tuning. Our model generates comparable samples to other approaches while using relatively simple neural networks, since the discrete latent variable carries much of the descriptive burden. Furthermore, the discrete latent provides significant control over generation.

PolyCNN: Learning Seed Convolutional Filters    

tl;dr PolyCNN only needs to learn one seed convolutional filter at each layer. This is an efficient variant of traditional CNN, with on-par performance.

In this work, we propose the polynomial convolutional neural network (PolyCNN), as a new design of a weight-learning efficient variant of the traditional CNN. The biggest advantage of the PolyCNN is that at each convolutional layer, only one convolutional filter is needed for learning the weights, which we call the seed filter, and all the other convolutional filters are the polynomial transformations of the seed filter, which is termed as an early fan-out. Alternatively, we can also perform late fan-out on the seed filter response to create the number of response maps needed to be input into the next layer. Both early and late fan-out allow the PolyCNN to learn only one convolutional filter at each layer, which can dramatically reduce the model complexity by saving 10x to 50x parameters during learning. While being efficient during both training and testing, the PolyCNN does not suffer performance due to the non-linear polynomial expansion which translates to richer representational power within the convolutional layers. By allowing direct control over model complexity, PolyCNN provides a flexible trade-off between performance and efficiency. We have verified the on-par performance between the proposed PolyCNN and the standard CNN on several visual datasets, such as MNIST, CIFAR-10, SVHN, and ImageNet.

Unsupervised one-to-many image translation    

tl;dr We train an image to image translation network that take as input the source image and a sample from a prior distribution to generate a sample from the target distribution

We perform completely unsupervised one-sided image to image translation between a source domain $X$ and a target domain $Y$ such that we preserve relevant underlying shared semantics (e.g., class, size, shape, etc). In particular, we are interested in a more difficult case than those typically addressed in the literature, where the source and target are ``far" enough that reconstruction-style or pixel-wise approaches fail. We argue that transferring (i.e., \emph{translating}) said relevant information should involve both discarding source domain-specific information while incorporate target domain-specific information, the latter of which we model with a noisy prior distribution. In order to avoid the degenerate case where the generated samples are only explained by the prior distribution, we propose to minimize an estimate of the mutual information between the generated sample and the sample from the prior distribution. We discover that the architectural choices are an important factor to consider in order to preserve the shared semantic between $X$ and $Y$. We show state of the art results on the MNIST to SVHN task for unsupervised image to image translation.

Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning    

tl;dr we propose a new framework for data-dependent DNN regularization that can prevent DNNs from overfitting random data or random labels.

Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the features are imposed. We find out that the reason for this is the inconsistency between the enforced geometry and the standard softmax cross entropy loss. To resolve this, we propose a new framework for data-dependent DNN regularization, the Geometrically-Regularized-Self-Validating neural Networks (GRSVNet). During training, the geometry enforced on one batch of features is simultaneously validated on a separate batch using a validation loss consistent with the geometry. We study a particular case of GRSVNet, the Orthogonal-Low-rank Embedding (OLE)-GRSVNet, which is capable of producing highly discriminative features residing in orthogonal low-rank subspaces. Numerical experiments show that OLE-GRSVNet outperforms DNNs with conventional regularization when trained on real data. More importantly, unlike conventional DNNs, OLE-GRSVNet refuses to memorize random data or random labels, suggesting it only learns intrinsic patterns by reducing the memorizing capacity of the baseline DNN.

Gradient Acceleration in Activation Functions    

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Dropout has been one of standard approaches to train deep neural networks, and it is known to regularize large models to avoid overfitting. The effect of dropout has been explained by avoiding co-adaptation. In this paper, however, we propose a new explanation of why dropout works and propose a new technique to design better activation functions. First, we show that dropout can be explained as an optimization technique to push the input towards the saturation area of nonlinear activation function by accelerating gradient information flowing even in the saturation area in backpropagation. Based on this explanation, we propose a new technique for activation functions, {\em gradient acceleration in activation function (GAAF)}, that accelerates gradients to flow even in the saturation area. Then, input to the activation function can climb onto the saturation area which makes the network more robust because the model converges on a flat region. Experiment results support our explanation of dropout and confirm that the proposed GAAF technique improves performances with expected properties.

On the Ineffectiveness of Variance Reduced Optimization for Deep Learning    

tl;dr The SVRG method fails on modern deep learning problems

The application of stochastic variance reduction to optimization has shown remarkable recent theoretical and practical success. The applicability of these techniques to the hard non-convex optimization problems encountered during training of modern deep neural networks is an open problem. We show that naive application of the SVRG technique and related approaches fail, and explore why.

PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation    

tl;dr We propose a new continuous control reinforcement learning method with a variance adaptation strategy inspired by the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization method

Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach. However, in continuous state and actions spaces and a Gaussian policy -- common in computer animation and robotics -- PPO is prone to getting stuck in local optima. In this paper, we observe a tendency of PPO to prematurely shrink the exploration variance, which naturally leads to slow progress. Motivated by this, we borrow ideas from CMA-ES, a black-box optimization method designed for intelligent adaptive Gaussian exploration, to derive PPO-CMA, a novel proximal policy optimization approach that expands the exploration variance on objective function slopes and only shrinks the variance when close to the optimum. This is implemented by using separate neural networks for policy mean and variance and training the mean and variance in separate passes. Our experiments demonstrate a clear improvement over vanilla PPO in many difficult OpenAI Gym MuJoCo tasks.


tl;dr We present a new approach, SNIP, that is simple, versatile and interpretable; it prunes irrelevant connections for a given task at single-shot prior to training and is applicable to a variety of neural network models without modifications.

Pruning large neural networks while maintaining the performance is often highly desirable due to the reduced space and time complexity. In existing methods, pruning is incorporated within an iterative optimization procedure with either heuristically designed pruning schedules or additional hyperparameters, undermining their utility. In this work, we present a new approach that prunes a given network once at initialization. Specifically, we introduce a saliency criterion based on connection sensitivity that identifies structurally important connections in the network for the given task even before training. This eliminates the need for both pretraining as well as the complex pruning schedule while making it robust to architecture variations. After pruning, the sparse network is trained in the standard way. Our method obtains extremely sparse networks with virtually the same accuracy as the reference network on image classification tasks and is broadly applicable to various architectures including convolutional, residual and recurrent networks. Unlike existing methods, our approach enables us to demonstrate that the retained connections are indeed relevant to the given task.

Pseudosaccades: A simple ensemble scheme for improving classification performance of deep nets    

tl;dr Inspired by saccades we describe a simple, cheap, effective way to improve deep net performance on an image labelling task.

We describe a simple ensemble approach that, unlike conventional ensembles, uses multiple random data sketches (‘pseudosaccades’) rather than multiple classifiers to improve classification performance. Using this simple, but novel, approach we obtain statistically significant improvements in classification performance on AlexNet, GoogLeNet, ResNet-50 and ResNet-152 baselines on Imagenet data – e.g. of the order of 0.3% to 0.6% in Top-1 accuracy and similar improvements in Top-k accuracy – essentially nearly for free.

A Guider Network for Multi-Dual Learning    

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A large amount of parallel data is needed to train a strong neural machine translation (NMT) system. This is a major challenge for low-resource languages. Building on recent work on unsupervised and semi-supervised methods, we propose a multi-dual learning framework to improve the performance of NMT by using an almost infinite amount of available monolingual data and some parallel data of other languages. Since our framework involves multiple languages and components, we further propose a timing optimization method that uses reinforcement learning (RL) to optimally schedule the different components in order to avoid imbalanced training. Experimental results demonstrate the validity of our model, and confirm its superiority to existing dual learning methods.


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Batch Normalization (BatchNorm) is an extremely useful component of modern neural network architectures, enabling optimization using higher learning rates and achieving faster convergence. In this paper, we use mean-field theory to analytically quantify the impact of BatchNorm on the geometry of the loss landscape for multi-layer networks consisting of fully-connected and convolutional layers. We show that it has a flattening effect on the loss landscape, as quantified by the maximum eigenvalue of the Fisher Information Matrix. These findings are then used to justify the use of larger learning rates for networks that use BatchNorm, and we provide quantitative characterization of the maximal allowable learning rate to ensure convergence. Experiments support our theoretically predicted maximum learning rate, and furthermore suggest that networks with smaller values of the BatchNorm parameter achieve lower loss after the same number of epochs of training.

Open-Ended Content-Style Recombination Via Leakage Filtering    

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We consider visual domains in which a class label specifies the content of an image, and class-irrelevant properties that differentiate instances constitute the style. We present a domain-independent method that permits the open-ended recombination of style of one image with the content of another. Open ended simply means that the method generalizes to style and content not present in the training data. The method starts by constructing a content embedding using an existing deep metric-learning technique. This trained content encoder is incorporated into a variational autoencoder (VAE), paired with a to-be-trained style encoder. The VAE reconstruction loss alone is inadequate to ensure a decomposition of the latent representation into style and content. Our method thus includes an auxiliary loss, leakage filtering, which ensures that no style information remaining in the content representation is used for reconstruction and vice versa. We synthesize novel images by decoding the style representation obtained from one image with the content representation from another. Using this method for data-set augmentation, we obtain state-of-the-art performance on few-shot learning tasks.

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication    

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Currently, progressively larger deep neural networks are trained on ever growing data corpora. In result, distributed training schemes are becoming increasingly relevant. A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general. %These challenges become even more pressing, as the number of computation nodes increases. To mitigate this problem we propose Sparse Binary Compression (SBC), a compression framework that allows for a drastic reduction of communication cost for distributed training. SBC combines existing techniques of communication delay and gradient sparsification with a novel binarization method and optimal weight update encoding to push compression gains to new limits. By doing so, our method also allows us to smoothly trade-off gradient sparsity and temporal sparsity to adapt to the requirements of the learning task. %We use tools from information theory to reason why SBC can achieve the striking compression rates observed in the experiments. Our experiments show, that SBC can reduce the upstream communication on a variety of convolutional and recurrent neural network architectures by more than four orders of magnitude without significantly harming the convergence speed in terms of forward-backward passes. For instance, we can train ResNet50 on ImageNet in the same number of iterations to the baseline accuracy, using $\times 3531$ less bits or train it to a $1\%$ lower accuracy using $\times 37208$ less bits. In the latter case, the total upstream communication required is cut from 125 terabytes to 3.35 gigabytes for every participating client. Our method also achieves state-of-the-art compression rates in a Federated Learning setting with 400 clients.

What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning    

tl;dr We propose a simple and effective imitation learning algorithm based on off-policy RL, which works well on image-based tasks and implicitly performs approximate inference of the expert policy.

Learning to imitate expert actions given demonstrations containing image observations is a difficult problem in robotic control. The key challenge is generalizing behavior to out-of-distribution states that differ from those in the demonstrations. State-of-the-art imitation learning algorithms perform well in environments with low-dimensional observations, but typically involve adversarial optimization procedures, which can be difficult to use with high-dimensional image observations. We propose a remarkably simple alternative based on off-policy reinforcement learning, which rewards the agent for matching demonstrated actions in demonstrated states — the key idea is initially filling the agent's experience replay buffer with demonstrations where rewards are set to a positive constant, and setting rewards to zero in all additional experiences. We derive this RL algorithm from first principles as a method for performing approximate inference under the MaxCausalEnt model of expert behavior — the approximate inference objective trades off between a pure behavioral cloning loss and a regularization term that incorporates information about state transitions via the soft Bellman error. Our experiments show that this algorithm matches the state of the art in low-dimensional environments, and significantly outperforms prior work in playing video games from high-dimensional images.

Disjoint Mapping Network for Cross-modal Matching of Voices and Faces    

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We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces. Different from the existing methods, DIMNet does not explicitly learn the joint relationship between the modalities. Instead, DIMNet learns a shared representation for different modalities by mapping them individually to their common covariates. These shared representations can then be used to find the correspondences between the modalities. We show empirically that DIMNet is able to achieve better performance than the current state-of-the-art methods, with the additional benefits of being conceptually simpler and less data-intensive.

Probabilistic Binary Neural Networks    

tl;dr We introduce a stochastic training method for training Binary Neural Network with both binary weights and activations.

Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a probabilistic training method for Neural Network with both binary weights and activations, called PBNet. By embracing stochasticity during training, we circumvent the need to approximate the gradient of functions for which the derivative is zero almost always, such as $\textrm{sign}(\cdot)$, while still obtaining a fully Binary Neural Network at test time. Moreover, it allows for anytime ensemble predictions for improved performance and uncertainty estimates by sampling from the weight distribution. Since all operations in a layer of the PBNet operate on random variables, we introduce stochastic versions of Batch Normalization and max pooling, which transfer well to a deterministic network at test time. We evaluate two related training methods for the PBNet: one in which activation distributions are propagated throughout the network, and one in which binary activations are sampled in each layer. Our experiments indicate that sampling the binary activations is an important element for stochastic training of binary Neural Networks.

NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search    

tl;dr An efficient multi-objective neural architecture search algorithm using NSGA-II

This paper introduces NSGA-Net, an evolutionary approach for neural architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a NAS procedure for multiple, possibly conflicting, objectives, (2) efficient exploration and exploitation of the space of potential neural network architectures, and (3) output of a diverse set of network architectures spanning a trade-off frontier of the objectives in a single run. NSGA-Net is a population-based search algorithm that explores a space of potential neural network architectures in three steps, namely, a population initialization step that is based on prior-knowledge from hand-crafted architectures, an exploration step comprising crossover and mutation of architectures and finally an exploitation step that applies the entire history of evaluated neural architectures in the form of a Bayesian Network prior. Experimental results suggest that combining the objectives of minimizing both an error metric and computational complexity, as measured by FLOPS, allows NSGA-Net to find competitive neural architectures near the Pareto front of both objectives on two different tasks, object classification and object alignment. NSGA-Net obtains networks that achieve 3.72% (at 4.5 million FLOP) error on CIFAR-10 classification and 8.64% (at 26.6 million FLOP) error on the CMU-Car alignment task.

Using Deep Siamese Neural Networks to Speed up Natural Products Research    

tl;dr We learn a direct mapping from NMR spectra of small molecules to a molecular structure based cluster space.

Natural products (NPs, compounds derived from plants and animals) are an important source of novel disease treatments. A bottleneck in the search for new NPs is structure determination. One method is to use 2D Nuclear Magnetic Resonance (NMR) imaging, which indicates bonds between nuclei in the compound, and hence is the "fingerprint" of the compound. Computing a similarity score between 2D NMR spectra for a novel compound and a compound whose structure is known helps determine the structure of the novel compound. Standard approaches to this problem do not appear to scale to larger databases of compounds. Here we use deep convolutional Siamese networks to map NMR spectra to a cluster space, where similarity is given by the distance in the space. This approach results in an AUC score that is more than four times better than an approach using Latent Dirichlet Allocation.

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks    

tl;dr A method for eliminating gradient variance and automatically tuning priors for effective training of bayesian neural networks

Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We fix VB and turn it into a robust inference tool for Bayesian neural networks. We achieve this with two innovations: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate strong predictive performance over alternative approaches.

Unsupervised Disentangling Structure and Appearance    

tl;dr We present a novel framework to learn the disentangled representation of structure and appearance in a completely unsupervised manner.

It is challenging to disentangle an object into two orthogonal spaces of structure and appearance since each can influence the visual observation in a different and unpredictable way. It is rare for one to have access to a large number of data to help separate the influences. In this paper, we present a novel framework to learn this disentangled representation in a completely unsupervised manner. We address this problem in a two-branch Variational Autoencoder framework. For the structure branch, we project the latent factor into a soft structured point tensor and constrain it with losses derived from prior knowledge. This encourages the branch to distill geometry information. Another branch learns the complementary appearance information. The two branches form an effective framework that can disentangle object's structure-appearance representation without any human annotation. We evaluate our approach on four image datasets, on which we demonstrate the superior disentanglement and visual analogy quality both in synthesis and real-world data. We are able to generate photo-realistic images with 256*256 resolution that are clearly disentangled in structure and appearance.

A Walk with SGD: How SGD Explores Regions of Deep Network Loss?    

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The non-convex nature of the loss landscape of deep neural networks (DNN) lends them the intuition that over the course of training, stochastic optimization algorithms explore different regions of the loss surface by entering and escaping many local minima due to the noise induced by mini-batches. But is this really the case? This question couples the geometry of the DNN loss landscape with how stochastic optimization algorithms like SGD interact with it during training. Answering this question may help us qualitatively understand the dynamics of deep neural network optimization. We show evidence through qualitative and quantitative experiments that mini-batch SGD rarely crosses barriers during DNN optimization. As we show, the mini-batch induced noise helps SGD explore different regions of the loss surface using a seemingly different mechanism. To complement this finding, we also investigate the qualitative reason behind the slowing down of this exploration when using larger batch-sizes. We show this happens because gradients from larger batch-sizes align more with the top eigenvectors of the Hessian, which makes SGD oscillate in the proximity of the parameter initialization, thus preventing exploration.

Learning and Data Selection in Big Datasets    

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Finding a dataset of minimal cardinality to characterize the optimal parameters of a model is of paramount importance in machine learning and distributed optimization over a network. This paper investigates the compressibility of large datasets. More specifically, we propose a framework that jointly learns the input-output mapping as well as the most representative samples of the dataset (sufficient dataset). Our analytical results show that the cardinality of the sufficient dataset increases sub-linearly with respect to the original dataset size. Numerical evaluations of real datasets reveal a large compressibility, up to 95%, without a noticeable drop in the learnability performance, measured by the generalization error.

Backdrop: Stochastic Backpropagation    

tl;dr We introduce backdrop, intuitively described as dropout acting on the backpropagation pipeline and find significant improvements in generalization for problems with non-decomposable losses and problems with multi-scale, hierarchical data structure.

We introduce backdrop, a flexible and simple-to-implement method, intuitively described as dropout acting only along the backpropagation pipeline. Backdrop is implemented via one or more masking layers which are inserted at specific points along the network. Each backdrop masking layer acts as the identity in the forward pass, but randomly masks parts of the backward gradient propagation. Intuitively, inserting a backdrop layer after any convolutional layer leads to stochastic gradients corresponding to features of that scale. Therefore, backdrop is well suited for problems in which the data have a multi-scale, hierarchical structure. Backdrop can also be applied to problems with non-decomposable loss functions where standard SGD methods are not well suited. We perform a number of experiments and demonstrate that backdrop leads to significant improvements in generalization.

Eidetic 3D LSTM: A Model for Video Prediction and Beyond    

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Spatiotemporal predictive learning, though long considered to be a promising self-supervised feature learning method, seldom shows its effectiveness beyond future video prediction. The reason is that it is difficult to learn good representations for both short-term frame dependency and long-term high-level relations. We present a new model, Eidetic 3D LSTM (E3D-LSTM), that integrates 3D convolutions into RNNs. The encapsulated 3D-Conv makes local perceptrons of RNNs motion aware and enables the memory cell to store better short-term features. For long-term relations, we make the present memory state interact with its historical records via a gate-controlled self-attention module. We describe this memory transition mechanism eidetic as it is able to effectively recall the stored memories across multiple timestamps even after long periods of disturbance. We first evaluate the spatiotemporal modeling capability of the E3D-LSTM model on two widely-used future video prediction datasets and achieve the state of the art performance. Then we demonstrate that with a self-supervised auxiliary learning strategy, the E3D-LSTM network performs well on early activity recognition to infer what is happening after observing only limited frames of video.

Overfitting Detection of Deep Neural Networks without a Hold Out Set    

tl;dr We introduce and analyze several criteria for detecting overfitting.

Overfitting is an ubiquitous problem in neural network training and usually mitigated using a holdout data set. Here we challenge this rationale and investigate criteria for overfitting without using a holdout data set. Specifically, we train a model for a fixed number of epochs multiple times with varying fractions of randomized labels and for a range of regularization strengths. A properly trained model should not be able to attain an accuracy greater than the fraction of properly labeled data points. Otherwise the model overfits. We introduce two criteria for detecting overfitting and one to detect underfitting. We analyze early stopping, the regularization factor, and network depth. In safety critical applications we are interested in models and parameter settings which perform well and are not likely to overfit. The methods of this paper allow characterizing and identifying such models.

Classification in the dark using tactile exploration    

tl;dr In this work, we study the problem of learning representations to identify novel objects by exploring objects using tactile sensing. Key point here is that the query is provided in image domain.

Combining information from different sensory modalities to execute goal directed actions is a key aspect of human intelligence. Specifically, human agents are very easily able to translate the task communicated in one sensory domain (say vision) into a representation that enables them to complete this task when they can only sense their environment using a separate sensory modality (say touch). In order to build agents with similar capabilities, in this work we consider the problem of a retrieving a target object from a drawer. The agent is provided with an image of a previously unseen object and it explores objects in the drawer using only tactile sensing to retrieve the object that was shown in the image without receiving any visual feedback. Success at this task requires close integration of visual and tactile sensing. We present a method for performing this task in a simulated environment using an anthropomorphic hand. We hope that future research in the direction of combining sensory signals for acting will find the object retrieval from a drawer to be a useful benchmark problem

The Natural Language Decathlon: Multitask Learning as Question Answering    

tl;dr We introduce a multitask learning challenge that spans ten natural language processing tasks and propose a new model that jointly learns them.

Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context. Furthermore, we present a new multitask question answering network (MQAN) that jointly learns all tasks in decaNLP without any task-specific modules or parameters more effectively than sequence-to-sequence and reading comprehension baselines. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN's multi-pointer-generator decoder is key to this success and that performance further improves with an anti-curriculum training strategy. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.


tl;dr We propose a self-attention based GAN architecture for unconditional text generation and improve on previous adversarial code-based results.

Inspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent code- based schemes in text generation. Adversarial latent code-based text generation has recently gained a lot of attention due to their promising results. In this paper, we take a step to fortify the architectures used in these setups, specifically AAE and ARAE. We benchmark two latent code-based methods (AAE and ARAE) designed based on adversarial setups. In our experiments, the Google sentence compression dataset is utilized to compare our method with these methods using various objective and subjective measures. The experiments demonstrate the proposed (self) attention-based models outperform the state-of-the-art in adversarial code-based text generation.

Graph Spectral Regularization For Neural Network Interpretability    

tl;dr Imposing graph structure on neural network layers for improved visual interpretability.

Deep neural networks can learn meaningful representations of data. However, these representations are hard to interpret. For example, visualizing a latent layer is generally only possible for at most three dimensions. Neural networks are able to learn and benefit from much higher dimensional representations but these are not visually interpretable because nodes have arbitrary ordering within a layer. Here, we utilize the ability of the human observer to identify patterns in structured representations to visualize higher dimensions. To do so, we propose a class of regularizations we call \textit{Graph Spectral Regularizations} that impose graph-structure on latent layers. This is achieved by treating activations as signals on a predefined graph and constraining those activations using graph filters, such as low pass and wavelet-like filters. This framework allows for any kind of graph as well as filter to achieve a wide range of structured regularizations depending on the inference needs of the data. First, we show a synthetic example that the graph-structured layer can reveal topological features of the data. Next, we show that a smoothing regularization can impose semantically consistent ordering of nodes when applied to capsule nets. Further, we show that the graph-structured layer, using wavelet-like spatially localized filters, can form localized receptive fields for improved image and biomedical data interpretation. In other words, the mapping between latent layer, neurons and the output space becomes clear due to the localization of the activations. Finally, we show that when structured as a grid, the representations create coherent images that allow for image-processing techniques such as convolutions.

Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning    

tl;dr We develop a new deep generative model for semi-supervised learning and propose a new Max-Min cross-entropy for training CNNs.

Unsupervised and semi-supervised learning are important problems that are especially challenging with complex data like natural images. Progress on these problems would accelerate if we had access to appropriate generative models under which to pose the associated inference tasks. Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Neural Rendering Model (NRM), a new hierarchical probabilistic generative model whose inference calculations correspond to those in a CNN. The NRM introduces a small set of latent variables at each level of the model and enforces dependencies among all the latent variables via a conjugate prior distribution. The conjugate prior yields a new regularizer for learning based on the paths rendered in the generative model for training CNNs–the Rendering Path Normalization (RPN). We demonstrate that this regularizer improves generalization both in theory and in practice. Likelihood estimation in the NRM yields the new Max-Min cross entropy training loss, which suggests a new deep network architecture–the Max- Min network–which exceeds or matches the state-of-art for semi-supervised and supervised learning on SVHN, CIFAR10, and CIFAR100.

Three Mechanisms of Weight Decay Regularization    

tl;dr We investigate weight decay regularization for different optimizers and identify three distinct mechanisms by which weight decay improves generalization.

Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of L2 regularization. Literal weight decay has been shown to outperform L2 regularization for optimizers for which they differ. We empirically investigate weight decay for three optimization algorithms (SGD, Adam, and KFAC) and a variety of network architectures. We identify three distinct mechanisms by which weight decay exerts a regularization effect, depending on the particular optimization algorithm and architecture: (1) increasing the effective learning rate, (2) regularizing approximated input-output Jacobian norm, and (3) reducing the effective damping coefficient for second-order optimization. Our results provide insight into how to improve the regularization of neural networks.

Local Critic Training of Deep Neural Networks    

tl;dr We propose a new learning algorithm of deep neural networks, which unlocks the layer-wise dependency of backpropagation.

This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks. In addition, the approach is also useful from multi-model perspectives, including structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters.

Automatic generation of object shapes with desired functionalities    

tl;dr It's difficult to make objects with desired affordances. We propose an automated method for generating object shapes with desired affordances, based on neural networks.

3D objects (artefacts) are made to fulfill functions. Designing an object often starts with defining a list of functionalities that it should provide, also known as functional requirements. Today, the design of 3D object models is still a slow and largely artisanal activity, with few CAD tools existing to aid the exploration of the design solution space. To accelerate the design process, we introduce an algorithm for generating object shapes with desired functionalities. Following the concept of form follows function, we assume that existing object shapes were rationally chosen to provide desired functionalities. First, we use an artificial neural network to learn a function-to-form mapping by analysing a dataset of objects labeled with their functionalities. Then, we combine forms providing one or more desired functions, generating an object shape that is expected to provide all of them. Finally, we verify in simulation whether the generated object possesses the desired functionalities, by defining and executing functionality tests on it.

ACIQ: Analytical Clipping for Integer Quantization of neural networks    

tl;dr We analyze the trade-off between quantization noise and clipping distortion in low precision networks, and show marked improvements over standard quantization schemes that normally avoid clipping

We analyze the trade-off between quantization noise and clipping distortion in low precision networks. We identify the statistics of various tensors, and derive exact expressions for the mean-square-error degradation due to clipping. By optimizing these expressions, we show marked improvements over standard quantization schemes that normally avoid clipping. For example, just by choosing the accurate clipping values, more than 40\% accuracy improvement is obtained for the quantization of VGG-16 to 4-bits of precision. Our results have many applications for the quantization of neural networks at both training and inference time.

Fake Sentence Detection as a Training Task for Sentence Encoding    

No tl;dr =[

Sentence encoders are typically trained on generative language modeling tasks with large unlabeled datasets. While these encoders achieve strong results on many sentence-level tasks, they are difficult to train with long training cycles. We introduce fake sentence detection as a new discriminative training task for learning sentence encoders. We automatically generate fake sentences by corrupting original sentences from a source collection and train the encoders to produce representations that are effective at detecting fake sentences. This binary classification task turns to be quite efficient for training sentence encoders. We compare a basic BiLSTM encoder trained on this task with strong sentence encoding models (Skipthought and FastSent) trained on a language modeling task. We find that the BiLSTM trains much faster on fake sentence detection (20 hours instead of weeks) using smaller amounts of data (1M instead of 64M sentences). Further analysis shows the learned representations also capture many syntactic and semantic properties expected from good sentence representations.

On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data    

tl;dr Three class priors are all you need to train deep models from only U data, while any two should not be enough.

Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM. We prove that it is impossible to estimate the risk of an arbitrary binary classifier in an unbiased manner given a single set of U data, but it becomes possible given two sets of U data with different class priors. These two facts answer a fundamental question---what the minimal supervision is for training any binary classifier from only U data. Following these findings, we propose an ERM-based learning method from two sets of U data, and then prove it is consistent. Experiments demonstrate the proposed method could train deep models and outperform state-of-the-art methods for learning from two sets of U data.

Bias Also Matters: Bias Attribution for Deep Neural Network Explanation    

tl;dr Attribute the bias terms of deep neural networks to input features by a backpropagation-type algorithm; Generate complementary and highly interpretable explanations of DNNs in addition to gradient-based attributions.

The gradient of a deep neural network (DNN) w.r.t. the input provides information that can be used to explain the output prediction in terms of the input features and has been widely studied to assist in interpreting DNNs. In a linear model (i.e., $g(x)=wx+b$), the gradient corresponds solely to the weights $w$. Such a model can reasonably locally linearly approximate a smooth nonlinear DNN, and hence the weights of this local model are the gradient. The other part, however, of a local linear model, i.e., the bias $b$, is usually overlooked in attribution methods since it is not part of the gradient. In this paper, we observe that since the bias in a DNN also has a non-negligible contribution to the correctness of predictions, it can also play a significant role in understanding DNN behaviors. In particular, we study how to attribute a DNN's bias to its input features. We propose a backpropagation-type algorithm ``bias back-propagation (BBp)'' that starts at the output layer and iteratively attributes the bias of each layer to its input nodes as well as combining the resulting bias term of the previous layer. This process stops at the input layer, where summing up the attributions over all the input features exactly recovers $b$. Together with the backpropagation of the gradient generating $w$, we can fully recover the locally linear model $g(x)=wx+b$. Hence, the attribution of the DNN outputs to its inputs is decomposed into two parts, the gradient $w$ and the bias attribution, providing separate and complementary explanations. We study several possible attribution methods applied to the bias of each layer in BBp. In experiments, we show that BBp can generate complementary and highly interpretable explanations of DNNs in addition to gradient-based attributions.

Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures    

tl;dr We show that rare but catastrophic failures may be missed entirely by random testing, which poses issues for safe deployment. Our proposed approach for adversarial testing fixes this.

This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. To this end, we focus on two problems: searching for scenarios when learned agents fail and the related problem of assessing their probability of failure. The standard method for agent evaluation in reinforcement learning, Vanilla Monte Carlo, can severely underestimate agent failure probabilities, leading to the deployment of unsafe agents. In our experiments, we observe this even after allocating equal compute to training and evaluation. To address this shortcoming, we draw upon the rare event probability estimation literature and propose an adversarial evaluation approach. Our approach focuses evaluation on difficult scenarios that are selected adversarially, while still providing unbiased estimates of failure probabilities. To do this, we propose a continuation approach to learning a failure probability predictor. This leverages data from related agents to overcome issues of data sparsity and allows the adversary to reuse data gathered for training the agent. We demonstrate the efficacy of adversarial evaluation on two complex reinforcement learning domains (humanoid control and simulated driving). Experimental results show that our methods can find catastrophic failures and estimate failures rates of agents multiple orders of magnitude faster (hours instead of days) than standard evaluation schemes.

Large Scale GAN Training for High Fidelity Natural Image Synthesis    

tl;dr GANs benefit from scaling up.

Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick", allowing fine control over the trade-off between sample fidelity and variety by truncating the latent space. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.3 and Frechet Inception Distance (FID) of 9.6, improving over the previous best IS of 52.52 and FID of 18.65.

Unsupervised Expectation Learning for Multisensory Binding    

tl;dr A hybrid deep neural network which adapts concepts of expectation learning for improving unisensory recognition using multisensory binding.

Expectation learning is a continuous learning process which uses known multisensory bindings to modulate unisensory perception. When perceiving an event, we have an expectation on what we should see or hear which affects our unisensory perception. Expectation learning is known to enhance the unisensory perception of previously known multisensory events. In this work, we present a novel hybrid deep recurrent model based on audio/visual autoencoders, for unimodal stimulus representation and reconstruction, and a recurrent self-organizing network for multisensory binding of the representations. The model adapts concepts of expectation learning to enhance the unisensory representation based on the learned bindings. We demonstrate that the proposed model is capable of reconstructing signals from one modality by processing input of another modality for 43,500 Youtube videos in the animal subset of the AudioSet corpus. Our experiments also show that when using expectation learning, the proposed model presents state-of-the-art performance in representing and classifying unisensory stimuli.

Total Style Transfer with a Single Feed-Forward Network    

tl;dr A paper suggesting a method to transform the style of images using deep neural networks.

Recent image style transferring methods achieved arbitrary stylization with input content and style images. To transfer the style of an arbitrary image to a content image, these methods used a feed-forward network with a lowest-scaled feature transformer or a cascade of the networks with a feature transformer of a corresponding scale. However, their approaches did not consider either multi-scaled style in their single-scale feature transformer or dependency between the transformed feature statistics across the cascade networks. This shortcoming resulted in generating partially and inexactly transferred style in the generated images. To overcome this limitation of partial style transfer, we propose a total style transferring method which transfers multi-scaled feature statistics through a single feed-forward process. First, our method transforms multi-scaled feature maps of a content image into those of a target style image by considering both inter-channel correlations in each single scaled feature map and inter-scale correlations between multi-scaled feature maps. Second, each transformed feature map is inserted into the decoder layer of the corresponding scale using skip-connection. Finally, the skip-connected multi-scaled feature maps are decoded into a stylized image through our trained decoder network.

Psychophysical vs. learnt texture representations in novelty detection    

tl;dr Comparison of psychophysical and CNN-encoded texture representations in a one-class neural network novelty detection application.

Parametric texture models have been applied successfully to synthesize artificial images. Psychophysical studies show that under defined conditions observers are unable to differentiate between model-generated and original natural textures. In industrial applications the reverse case is of interest: a texture analysis system should decide if human observers are able to discriminate between a reference and a novel texture. For example, in case of inspecting decorative surfaces the de- tection of visible texture anomalies without any prior knowledge is required. Here, we implemented a human-vision-inspired novelty detection approach. Assuming that the features used for texture synthesis are important for human texture percep- tion, we compare psychophysical as well as learnt texture representations based on activations of a pretrained CNN in a novelty detection scenario. Additionally, we introduce a novel objective function to train one-class neural networks for novelty detection and compare the results to standard one-class SVM approaches. Our experiments clearly show the differences between human-vision-inspired texture representations and learnt features in detecting visual anomalies. Based on a dig- ital print inspection scenario we show that psychophysical texture representations are able to outperform CNN-encoded features.


tl;dr We propose a novel autoencoding model with augmented adversarial reconstruction loss. We intoduce new metric for content-based assessment of reconstructions.

We propose a novel autoencoding model called Pairwise Augmented GANs. We train a generator and an encoder jointly and in an adversarial manner. The generator network learns to sample realistic objects. In turn the encoder network at the same time in turn is trained to map the true data distribution to the prior in a latent space. To ensure good reconstructions we introduce an augmented adversarial reconstruction loss. Here we train a discriminator to distinguish two types of pairs: the object with its augmentation and the one with its reconstruction. We show that such adversarial loss compares objects based on the content rather than on the exact match. We experimentally demonstrate that our model generates samples and reconstructions of quality competitive with state-of-the-art on datasets MNIST, CIFAR10, CelebA and achieves good quantitative results on CIFAR10.

Cutting Down Training Memory by Re-fowarding    

tl;dr This paper proposes fundamental theory and optimal algorithms for DNN training, which reduce up to 80% of training memory for popular DNNs.

Deep Neutral Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory as a hardware resource is always finite, which limits the image resolution, batch size, and learning rate that could be used for better DNN performance. In this paper, we propose a novel training approach, called Re-forwarding, that substantially reduces memory usage in training. Our approach automatically finds a subset of layers in DNNs, and stores tensors only at these layers during the first forward. During backward, extra local forwards (called the Re-forwarding process) are conducted to compute the missing tensors between the subset of layers. The total memory cost becomes the sum of (1) the memory cost at the subset of layers and (2) the maximum memory cost among local re-forwards. Re-forwarding trades training time for memory and does not compromise any performance in testing. We propose theories and algorithms that achieve the optimal memory solutions for DNNs with either linear or arbitrary computation graphs. Experiments show that Re-forwarding cuts down up-to 80% of training memory on popular DNNs such as Alexnet, VGG, ResNet, Densenet and Inception net.

Predictive Local Smoothness for Stochastic Gradient Methods    

No tl;dr =[

Stochastic gradient methods are dominant in nonconvex optimization especially for deep models but have low asymptotical convergence due to the fixed smoothness. To address this problem, we propose a simple yet effective method for improving stochastic gradient methods named predictive local smoothness (PLS). First, we create a convergence condition to build a learning rate varied adaptively with local smoothness. Second, the local smoothness can be predicted by the latest gradients. Third, we use the adaptive learning rate to update the stochastic gradients for exploring linear convergence rates. By applying the PLS method, we implement new variants of three popular algorithms: PLS-stochastic gradient descent (PLS-SGD), PLS-accelerated SGD (PLS-AccSGD), and PLS-AMSGrad. Moreover, we provide much simpler proofs to ensure their linear convergence. Empirical results show that our variants have better performance gains than the popular algorithms, such as, faster convergence and alleviating explosion and vanish of gradients.

signSGD via Zeroth-Order Oracle    

tl;dr We design and analyze a new zeroth-order stochastic optimization algorithm, ZO-signSGD, and demonstrate its connection and application to black-box adversarial attacks in robust deep learning

In this paper, we design and analyze a new zeroth-order (ZO) stochastic optimization algorithm, ZO-signSGD, which enjoys dual advantages of gradient-free operations and signSGD. The latter requires only the sign information of gradient estimates but is able to achieve a comparable or even better convergence speed than SGD-type algorithms. Our study shows that ZO signSGD requires $\sqrt{d}$ times more iterations than signSGD, leading to a convergence rate of $O(\sqrt{d}/\sqrt{T})$ under mild conditions, where $d$ is the number of optimization variables, and $T$ is the number of iterations. In addition, we analyze the effects of different types of gradient estimators on the convergence of ZO-signSGD, and propose two variants of ZO-signSGD that at least achieve $O(\sqrt{d}/\sqrt{T})$ convergence rate. On the application side we explore the connection between ZO-signSGD and black-box adversarial attacks in robust deep learning. Our empirical evaluations on image classification datasets MNIST and CIFAR-10 demonstrate the superior performance of ZO-signSGD on the generation of adversarial examples from black-box neural networks.

Ain't Nobody Got Time for Coding: Structure-Aware Program Synthesis from Natural Language    

tl;dr We generate source code based on short descriptions in natural language, using deep neural networks.

Program synthesis from natural language (NL) is practical for humans and, once technically feasible, would significantly facilitate software development and revolutionize end-user programming. We present SAPS, an end-to-end neural network capable of mapping relatively complex, multi-sentence NL specifications to snippets of executable code. The proposed architecture relies exclusively on neural components, and is built upon a tree2tree autoencoder trained on abstract syntax trees, combined with a pretrained word embedding and a bi-directional multi-layer LSTM for NL processing. The decoder features a doubly-recurrent LSTM with a novel signal propagation scheme and soft attention mechanism. When applied to a large dataset of problems proposed in a previous study, SAPS performs on par with or better than the method proposed there, producing correct programs in over 90% of cases. In contrast to other methods, it does not involve any non-neural components to post-process the resulting programs, and uses a fixed-dimensional latent representation as the only link between the NL analyzer and source code generator.

Fixing Posterior Collapse with delta-VAEs    

tl;dr Avoid posterior collapse by lower bounding the rate.

Due to the phenomenon of “posterior collapse”, current latent variable generativemodels pose a challenging design choice which trades-off optimizing the ELBObut handicapping the decoders’ capacity and expressivity, or changing the loss tosomething that is not directly minimizing the description length. In this paper wepropose an alternative that utilizes the best, most powerful generative models asdecoders, whilst optimizing the proper variational lower bound all while ensuringthat the latent variables preserve and encode useful information. delta-VAEs pro-posed here achieve this by constraining the variational family for the posterior tohave a minimum distance to the prior, which resembles the classic representationlearning approach of slow feature analysis. We demonstrate the efficacy of our ap-proach at modeling images: learning representations, improving sample quality,and improving state of the art log-likelihood on CIFAR-10 and ImageNet32×32.

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees    

tl;dr We design model-based reinforcement learning algorithms with theoretical guarantees and achieve state-of-the-art results on Mujuco benchmark tasks when one million or fewer samples are permitted.

Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical understanding of such methods has been rather limited. This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees. We design a meta-algorithm with a theoretical guarantee of monotone improvement to a local maximum of the expected reward. The meta-algorithm iteratively builds a lower bound of the expected reward based on the estimated dynamical model and sample trajectories, and then maximizes the lower bound jointly over the policy and the model. The framework extends the optimism-in-face-of-uncertainty principle to non-linear dynamical models in a way that requires no explicit uncertainty quantification. Instantiating our framework with simplification gives a variant of model-based RL algorithms Stochastic Lower Bounds Optimization (SLBO). Experiments demonstrate that SLBO achieves the state-of-the-art performance when only 1M or fewer samples are permitted on a range of continuous control benchmark tasks.

Knowledge Flow: Improve Upon Your Teachers    

tl;dr ‘Knowledge Flow’ trains a deep net (student) by injecting information from multiple nets (teachers). The student is independent upon training and performs very well on learned tasks irrespective of the setting (reinforcement or supervised learning).

A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an initialization for fine-tuning a new model. To address this issue, in this paper, we develop knowledge flow which moves ‘knowledge’ from multiple deep nets, referred to as teachers, to a new deep net model, called the student. The structure of the teachers and the student can differ arbitrarily and they can be trained on entirely different tasks with different output spaces too. Upon training with knowledge flow the student is independent of the teachers. We demonstrate our approach on a variety of supervised and reinforcement learning tasks, outperforming fine-tuning and other ‘knowledge exchange’ methods.

Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures    

No tl;dr =[

Deep artificial neural networks with spatially repeated processing (a.k.a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in the primate ventral visual processing stream. Over the past five years, these ANNs have evolved from a simple feedforward eight-layer architecture in AlexNet to extremely deep and branching NASNet architectures, demonstrating increasingly better object categorization performance. Here we ask, as ANNs have continued to evolve in performance, are they also strong candidate models for the brain? To answer this question, we developed Brain-Score, a composite of neural and behavioral benchmarks that score any ANN on how brain-like it is, together with an online platform where ANNs can be submitted to receive a Brain-Score and their rank relative to other models. Deploying our framework on dozens of state-of-the-art ANNs, we found that ResNet and DenseNet families of models are the closest models from the Machine Learning community to primate ventral visual stream. Curiously, best current ImageNet models, such as PNASNet, were not the top-performing models on Brain-Score. Despite high scores, these deep models are often hard to map onto the brain's anatomy due to their vast number of layers and missing biologically-important connections, such as recurrence. To further map onto anatomy and validate our approach, we built CORnet-S: a neural network developed by using Brain-Score as a guide with the anatomical constraints of compactness and recurrence. Although a shallow model with four anatomically mapped areas with recurrent connectivity, CORnet-S is a top model on Brain-Score and outperforms similarly compact models on ImageNet.


tl;dr We develop a statistical-geometric unsupervised learning augmentation framework for deep neural networks to make them robust to adversarial attacks.

We introduce a novel geometric perspective and unsupervised model augmentation framework for transforming traditional deep (convolutional) neural networks into adversarially robust classifiers. Class-conditional probability densities based on Bayesian nonparametric mixtures of factor analyzers (BNP-MFA) over the input space are used to design soft decision labels for feature to label isometry. Classconditional distributions over features are also learned using BNP-MFA to develop plug-in maximum a posterior (MAP) classifiers to replace the traditional multinomial logistic softmax classification layers. This novel unsupervised augmented framework, which we call geometrically robust networks (GRN), is applied to CIFAR-10, CIFAR-100, and to Radio-ML (a time series dataset for radio modulation recognition). We demonstrate the robustness of GRN models to adversarial attacks from fast gradient sign method, Carlini-Wagner, and projected gradient descent.

Pix2Scene: Learning Implicit 3D Representations from Images    

tl;dr pix2scene: a deep generative based approach for implicitly modelling the geometrical properties of a 3D scene from images

Modelling 3D scenes from 2D images is a long-standing problem in computer vision with implications in, e.g., simulation and robotics. We propose pix2scene, a deep generative-based approach that implicitly models the geometric properties of a scene from images. Our method learns the depth and orientation of scene points visible in images. Our model can then predict the structure of a scene from various, previously unseen view points. It relies on a bi-directional adversarial learning mechanism to generate scene representations from a latent code, inferring the 3D representation of the underlying scene geometry. We showcase a novel differentiable renderer to train the 3D model in an end-to-end fashion, using only images. We demonstrate the generative ability of our model qualitatively on both a custom dataset and on ShapeNet. Finally, we evaluate the effectiveness of the learned 3D scene representation in supporting a 3D spatial reasoning.


tl;dr We provide new insights and interpretations of RNNs from a max-affine spline operators perspective

We develop a framework for understanding and improving recurrent neural net-works (RNNs) using max-affine spline operators (MASO). We prove that RNNsusing piecewise affine and convex nonlinearities can be written as a simple piece-wise affine spline operator. The resulting representation provides several new per-spectives for analyzing RNNs, three of which we study in this paper. First, weshow that an RNN internally partitions the input space during training using vec-tor quantization and that it builds up the partition through time. Second, we showthat the affine parameter of an RNN corresponds to an input-specific template,from which we can interpret an RNN as performing a simple template matching(matched filtering) given the input. Third, by closely examining the MASO RNN formula, we prove that injecting Gaussian noise in the initial hidden state in RNNs corresponds to an explicit L2regularization on the affine parameters, which links to exploding gradient issues and improves generalization. Extensive experimentson several datasets of various modalities demonstrates and validates each of theabove analyses. In particular, using initial hidden states elevates simple RNNs tostate-of-the-art performance on these datasets

Rotation Equivariant Networks via Conic Convolution and the DFT    

tl;dr We propose conic convolution and the 2D-DFT to encode rotation equivariance into an neural network.

Performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Many image classification tasks, such as those related to cellular imaging, exhibit invariance to rotation. In particular, to aid convolutional neural networks in learning rotation invariance, we consider a simple, efficient conic convolutional scheme that encodes rotational equivariance, along with a method for integrating the magnitude response of the 2D-discrete-Fourier transform (2D-DFT) to encode global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). We evaluated the efficacy of CFNet as compared to a standard CNN and group-equivariant CNN (G-CNN) for several different image classification tasks and demonstrated improved performance, including classification accuracy, computational efficiency, and its robustness to hyperparameter selection. Taken together, we believe CFNet represents a new scheme that has the potential to improve many imaging analysis applications.

Better Accuracy with Quantified Privacy: Representations Learned via Reconstructive Adversarial Network    

No tl;dr =[

The remarkable success of machine learning, especially deep learning, has produced a variety of cloud-based services for mobile users. Such services require an end user to send data to the service provider, which presents a serious challenge to end-user privacy. To address this concern, prior works either add noise to the data or send features extracted from the raw data. They struggle to balance between the utility and privacy because added noise reduces utility and raw data can be reconstructed from extracted features. This work represents a methodical departure from prior works: we balance between a measure of privacy and another of utility by leveraging adversarial learning to find a sweeter tradeoff. We design an encoder that optimizes against the reconstruction error (a measure of privacy), adversarially by a Decoder, and the inference accuracy (a measure of utility) by a Classifier. The result is RAN, a novel deep model with a new training algorithm that automatically extracts features for classification that are both private and useful. It turns out that adversarially forcing the extracted features to only conveys the intended information required by classification leads to an implicit regularization leading to better classification accuracy than the original model which completely ignores privacy. Thus, we achieve better privacy with better utility, a surprising possibility in machine learning! We conducted extensive experiments on five popular datasets over four training schemes, and demonstrate the superiority of RAN compared with existing alternatives.

Visualizing and Discovering Behavioural Weaknesses in Deep Reinforcement Learning    

tl;dr We present a method to synthesize states of interest for reinforcement learning agents in order to analyze their behavior.

As deep reinforcement learning is being applied to more and more tasks, there is a growing need to better understand and probe the learned agents. Visualizing and understanding the decision making process can be very valuable to comprehend and identify problems in the learned behavior. However, this topic has been relatively under-explored in the reinforcement learning community. In this work we present a method for synthesizing states of interest for a trained agent. Such states could be situations (e.g. crashing or damaging a car) in which specific actions are necessary. Further, critical states in which a very high or a very low reward can be achieved (e.g. risky states) are often interesting to understand the situational awareness of the system. To this end, we learn a generative model over the state space of the environment and use its latent space to optimize a target function for the state of interest. In our experiments we show that this method can generate insightful visualizations for a variety of environments and reinforcement learning methods. We explore these issues in the standard Atari benchmark games as well as in an autonomous driving simulator. Based on the efficiency with which we have been able to identify significant decision scenarios with this technique, we believe this general approach could serve as an important tool for AI safety applications.

Learning to Learn with Conditional Class Dependencies    

tl;dr CAML is an instance of MAML with conditional class dependencies.

Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning. Although some label structure can implicitly be obtained when training on huge amounts of data, in a few-shot learning context where little data is available, making explicit use of the label structure can inform the model to reshape the representation space to reflect a global sense of class dependencies. We propose a meta-learning framework, Conditional class-Aware Meta-Learning (CAML), that conditionally transforms feature representations based on a metric space that is trained to capture inter-class dependencies. This enables a conditional modulation of the feature representations of the base-learner to impose regularities informed by the label space. Experiments show that the conditional transformation in CAML leads to more disentangled representations and achieves competitive results on the miniImageNet benchmark.

Hierarchical Visuomotor Control of Humanoids    

No tl;dr =[

We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision. We develop an architecture capable of surprisingly flexible, task-directed motor control of a relatively high-DoF humanoid body by combining pre-training of low-level motor controllers with a high-level, task-focused controller that switches among low-level sub-policies. The resulting system is able to control a physically-simulated humanoid body to solve tasks that require coupling visual perception from an unstabilized egocentric RGB camera during locomotion in the environment. Supplementary video link:

Learning Kolmogorov Models for Binary Random Variables    

No tl;dr =[

We propose a framework for learning a Kolmogorov model, for a collection of binary random variables. More specifically, we derive conditions that link (in the sense of implications in mathematical logic) outcomes of specific random variables and extract valuable relations from the data. We also propose an efficient algorithm for computing the model and show its first-order optimality, despite the combinatorial nature of the learning problem. We exemplify our general framework to recommendation systems and gene expression data. We believe that the work is a significant step toward interpretable machine learning.

A Case for Object Compositionality in Deep Generative Models of Images    

tl;dr We propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition

Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several core inductive biases. However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked. In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition. This provides a way to efficiently learn a more accurate generative model of real-world images, and serves as an initial step towards learning corresponding object representations. We evaluate our approach on several multi-object image datasets, and find that the generator learns to identify and disentangle information corresponding to different objects at a representational level. A human study reveals that the resulting generative model is better at generating images that are more faithful to the reference distribution.

Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation    

tl;dr Presents new architecture which leverages information globalization power of u-nets in a deeper networks and performs well across tasks without any bells and whistles.

Many imaging tasks require global information about all pixels in an image. Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and down-sampled into a single output. But for semantic segmentation and object detection tasks, a network must provide higher-resolution pixel-level outputs. To globalize information while preserving resolution, many researchers propose the inclusion of sophisticated auxiliary blocks, but these come at the cost of a considerable increase in network size and computational cost. This paper proposes stacked u-nets (SUNets), which iteratively combine features from different resolution scales while maintaining resolution. SUNets leverage the information globalization power of u-nets in a deeper net- work architectures that is capable of handling the complexity of natural images. SUNets perform extremely well on semantic segmentation tasks using a small number of parameters.

Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model    

tl;dr State-of-the-art zero-shot learning performance by using a translation task to bridge multi-task training across languages.

Neural language models have been shown to achieve an impressive level of performance on a number of language processing tasks. The majority of these models, however, are limited to producing predictions for only English texts due to limited amounts of labeled data available in other languages. One potential method for overcoming this issue is learning cross-lingual text representations that can be used to transfer the performance from training on English tasks to non-English tasks, despite little to no task-specific non-English data. In this paper, we explore a natural setup for learning cross-lingual sentence representations: the dual-encoder. We provide a comprehensive evaluation of our cross-lingual representations on a number of monolingual, cross-lingual, and zero-shot/few-shot learning tasks, and also give an analysis of different learned cross-lingual embedding spaces.


tl;dr GANs are shown to provide us a new effective robust mean estimate against agnostic contaminations with both statistical optimality and practical tractability.

Robust estimation under Huber's $\epsilon$-contamination model has become an important topic in statistics and theoretical computer science. Rate-optimal procedures such as Tukey's median and other estimators based on statistical depth functions are impractical because of their computational intractability. In this paper, we establish an intriguing connection between f-GANs and various depth functions through the lens of f-Learning. Similar to the derivation of f-GAN, we show that these depth functions that lead to rate-optimal robust estimators can all be viewed as variational lower bounds of the total variation distance in the framework of f-Learning. This connection opens the door of computing robust estimators using tools developed for training GANs. In particular, we show that a JS-GAN that uses a neural network discriminator with at least one hidden layer is able to achieve the minimax rate of robust mean estimation under Huber's $\epsilon$-contamination model. Interestingly, the hidden layers of the neural net structure in the discriminator class are shown to be necessary for robust estimation.

Generative model based on minimizing exact empirical Wasserstein distance    

tl;dr We have proposed a flexible generative model that learns stably by directly minimizing exact empirical Wasserstein distance.

Generative Adversarial Networks (GANs) are a very powerful framework for generative modeling. However, they are often hard to train, and learning of GANs often becomes unstable. Wasserstein GAN (WGAN) is a promising framework to deal with the instability problem as it has a good convergence property. One drawback of the WGAN is that it evaluates the Wasserstein distance in the dual domain, which requires some approximation, so that it may fail to optimize the true Wasserstein distance. In this paper, we propose evaluating the exact empirical optimal transport cost efficiently in the primal domain and performing gradient descent with respect to its derivative to train the generator network. Experiments on the MNIST dataset show that our method is significantly stable to converge, and achieves the lowest Wasserstein distance among the WGAN variants at the cost of some sharpness of generated images. Experiments on the 8-Gaussian toy dataset show that better gradients for the generator are obtained in our method. In addition, the proposed method enables more flexible generative modeling than WGAN.

Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality    

tl;dr We propose a new metric for evaluating GAN models.

Generative Adversarial Networks (GANs) are an elegant mechanism for data generation. However, a key challenge when using GANs is how to best measure their ability to generate realistic data. In this paper, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an effective evaluation metric for GAN quality. In particular, we propose a new evaluation measure, CrossLID, that assesses the local intrinsic dimensionality (LID) of input data with respect to neighborhoods within GAN-generated samples. In experiments on 3 benchmark image datasets, we compare our proposed measure to several state-of-the-art evaluation metrics. Our experiments show that CrossLID is strongly correlated with sample quality, is sensitive to mode collapse, is robust to small-scale noise and image transformations, and can be applied in a model-free manner. Furthermore, we show how CrossLID can be used within the GAN training process to improve generation quality.

Deep clustering based on a mixture of autoencoders    

tl;dr We propose a deep clustering method where instead of a centroid each cluster is represented by an autoencoder

In this paper we propose a Deep Autoencoder Mixture Clustering (DAMIC) algorithm. It is based on a mixture of deep autoencoders where each cluster is represented by an autoencoder. A clustering network transforms the data into another space and then selects one of the clusters. Next, the autoencoder associated with this cluster is used to reconstruct the data-point. The clustering algorithm jointly learns the nonlinear data representation and the set of autoencoders. The optimal clustering is found by minimizing the reconstruction loss of the mixture of autoencoder network. Unlike other deep clustering algorithms, no regularization term is needed to avoid data collapsing to a single point. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.

INVASE: Instance-wise Variable Selection using Neural Networks    

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The advent of big data brings with it data with more and more dimensions and thus a growing need to be able to efficiently select which features to use for a variety of problems. While global feature selection has been a well-studied problem for quite some time, only recently has the paradigm of instance-wise feature selection been developed. In this paper, we propose a new instance-wise feature selection method, which we term INVASE. INVASE consists of 3 neural networks, a selector network, a predictor network and a baseline network which are used to train the selector network using the actor-critic methodology. Using this methodology, INVASE is capable of flexibly discovering feature subsets of a different size for each instance, which is a key limitation of existing state-of-the-art methods. We demonstrate through a mixture of synthetic and real data experiments that INVASE significantly outperforms state-of-the-art benchmarks.

Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach    

tl;dr We obtain non-vacuous generalization bounds on ImageNet-scale deep neural networks by combining an original PAC-Bayes bound and an off-the-shelf neural network compression method.

Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data. Nevertheless, these networks often generalize well in practice. It has also been observed that trained networks can often be ``compressed to much smaller representations. The purpose of this paper is to connect these two empirical observations. Our main technical result is a generalization bound for compressed networks based on the compressed size that, combined with off-the-shelf compression algorithms, leads to state-of-the-art generalization guarantees. In particular, we provide the first non-vacuous generalization guarantees for realistic architectures applied to the ImageNet classification problem. Additionally, we show that compressibility of models that tend to overfit is limited. Empirical results show that an increase in overfitting increases the number of bits required to describe a trained network.

Information Regularized Neural Networks    

tl;dr we propose a regularizer that improves the classification performance of neural networks

We formulate an information-based optimization problem for supervised classification. For invertible neural networks, the control of these information terms is passed down to the latent features and parameter matrix in the last fully connected layer, given that mutual information is invariant under invertible map. We propose an objective function and prove that it solves the optimization problem. Our framework allows us to learn latent features in an more interpretable form while improving the classification performance. We perform extensive quantitative and qualitative experiments in comparison with the existing state-of-the-art classification models.

Surprising Negative Results for Generative Adversarial Tree Search    

tl;dr Surprising negative results on Model Based + Model deep RL

Although many recent advances in deep reinforcement learning consist of model- free methods, model-based approaches remain an alluring prospect owing to their potential to exploit unsupervised data to learn environment dynamics. Moreover, with new breakthroughs on image-to-image transduction, Pix2Pix GANs are a natural choice for learning to predict the dynamics of environments where ob- servations consist of images (like Atari games). Inspired by AlphaGo, which combines model-based and model-free RL, we propose generative adversarial tree search (GATS), simulating roll-outs with a learned GAN-based dynamics model and reward predictor. We theoretically prove some favorable properties of GATS vis-a-vis the bias-variance trade-off. The approach combines model-based planning via MCTS with model-free learning with DQNs. Empirically, on 5 popular Atari games, despite the dynamics and reward predictors converging quickly to accurate solutions GATS fails to outperform DQNs. We present a hypothesis for why tree search with short roll-outs can fail even given perfect modelling.

Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations    

tl;dr We improve GAIL by learning discriminators using multiclass classification with non-expert regarded as an extra class.

Imitation learning aims to learn an optimal policy from expert demonstrations and its recent combination with deep learning has shown impressive performance. However, collecting a large number of expert demonstrations for deep learning is time-consuming and requires much expert effort. In this paper, we propose a method to improve generative adversarial imitation learning by using additional information from non-expert demonstrations which are easier to obtain. The key idea of our method is to perform multiclass classification to learn discriminator functions where non-expert demonstrations are regarded as being drawn from an extra class. Experiments in continuous control tasks demonstrate that our method learns optimal policies faster and has more stable performance than the generative adversarial imitation learning baseline.

Deepström Networks    

tl;dr A new neural architecture where top dense layers of standard convolutional architectures are replaced with an approximation of a kernel function by relying on the Nyström approximation.

Recent work has focused on combining kernel methods and deep learning. With this in mind, we introduce Deepström networks -- a new architecture of neural networks which we use to replace top dense layers of standard convolutional architectures with an approximation of a kernel function by relying on the Nyström approximation. Our approach is easy highly flexible. It is compatible with any kernel function and it allows exploiting multiple kernels. We show that Deepström networks reach state-of-the-art performance on standard datasets like SVHN and CIFAR100. One benefit of the method lies in its limited number of learnable parameters which make it particularly suited for small training set sizes, e.g. from 5 to 20 samples per class. Finally we illustrate two ways of using multiple kernels, including a multiple Deepström setting, that exploits a kernel on each feature map output by the convolutional part of the model.

Sequenced-Replacement Sampling for Deep Learning    

tl;dr Proposed a novel way (without adding new parameters) of training deep neural network in order to improve generalization, especially for the case where we have relatively small images-per-class.

We propose sequenced-replacement sampling (SRS) for training deep neural networks. The basic idea is to assign a fixed sequence index to each sample in the dataset. Once a mini-batch is randomly drawn in each training iteration, we refill the original dataset by successively adding samples according to their sequence index. Thus we carry out replacement sampling but in a batched and sequenced way. In a sense, SRS could be viewed as a way of performing "mini-batch augmentation". It is particularly useful for a task where we have a relatively small images-per-class such as CIFAR-100. Together with a longer period of initial large learning rate, it significantly improves the classification accuracy in CIFAR-100 over the current state-of-the-art results. Our experiments indicate that training deeper networks with SRS is less prone to over-fitting. In the best case, we achieve an error rate as low as 10.10%.

Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks    

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The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles. There is also clear evidence that the activation function (e.g. the rectifier and the LSTM units) plays a crucial role in the complexity of learning. Based on this remark, this paper discusses an optimal selection of the neuron non-linearity in a functional framework that is inspired from classic regularization arguments. A representation theorem is given which indicates that the best activation function is a kernel expansion in the training set, that can be effectively approximated over an opportune set of points modeling 1-D clusters. The idea can be naturally extended to recurrent networks, where the expressiveness of kernel-based activation functions turns out to be a crucial ingredient to capture long-term dependencies. We give experimental evidence of this property by a set of challenging experiments, where we compare the results with neural architectures based on state of the art LSTM cells.


tl;dr Multi-timestep parallelizable acoustic modeling with diagonal LSTM, QRNN and Gated ConvNet

Real-time speech recognition on mobile and embedded devices is an important application of neural networks. Acoustic modeling is the fundamental part of speech recognition and is usually implemented with long short-term memory (LSTM)-based recurrent neural networks (RNNs). However, the single thread execution of an LSTM RNN is extremely slow in most embedded devices because the algorithm needs to fetch a large number of parameters from the DRAM for computing each output sample. We explore a few acoustic modeling algorithms that can be executed very efficiently on embedded devices. These algorithms reduce the overhead of memory accesses using multi-timestep parallelization that computes multiple output samples at a time by reading the parameters only once from the DRAM. The algorithms considered are the quasi RNNs (QRNNs), Gated ConvNets, and diagonalized LSTMs. In addition, we explore neural networks that equip one-dimensional (1-D) convolution at each layer of these algorithms, and by which can obtain a very large performance increase in the QRNNs and Gated ConvNets. The experiments were conducted using two tasks, one is the connectionist temporal classification (CTC)-based end-to-end speech recognition on WSJ corpus and the other is the phoneme classification on TIMIT dataset. We not only significantly increase the execution speed but also obtain a much higher accuracy, compared to LSTM RNN-based modeling. Thus, this work can be applicable not only to embedded system-based implementations but also to server-based ones.

End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition    

tl;dr We consider a simplified deep convolutional neural network model. We show that all layers of this network can be approximately learned with a proper application of tensor decomposition.

In this paper we study the problem of learning the weights of a deep convolutional neural network. We consider a network where convolutions are carried out over non-overlapping patches with a single kernel in each layer. We develop an algorithm for simultaneously learning all the kernels from the training data. Our approach dubbed Deep Tensor Decomposition (DeepTD) is based on a rank-1 tensor decomposition. We theoretically investigate DeepTD under a realizable model for the training data where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted convolutional kernels. We show that DeepTD is data-efficient and provably works as soon as the sample size exceeds the total number of convolutional weights in the network. Our numerical experiments demonstrate the effectiveness of DeepTD and verify our theoretical findings.

Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks    

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Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". We design a new algorithm, called Partially adaptive momentum estimation method (Padam), which unifies the Adam/Amsgrad with SGD by introducing a partial adaptive parameter p, to achieve the best from both worlds. Experiments on standard benchmarks show that Padam can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks.

Advocacy Learning    

tl;dr We introduce a method that encourages different components in a networks to compete, and show that this can improve attention quality.

We introduce advocacy learning, a novel supervised training scheme for classification problems. This training scheme applies to a framework consisting of two connected networks: 1) the Advocates, composed of one subnetwork per class, which take the input and provide a convincing class-conditional argument in the form of an attention map, and 2) a Judge, which predicts the inputs class label based on these arguments. Each Advocate aims to convince the Judge that the input example belongs to their corresponding class. In contrast to a standard network, in which all subnetworks are trained to jointly cooperate, we train the Advocates to competitively argue for their class, even when the input belongs to a different class. We also explore a variant, honest advocacy learning, where the Advocates are only trained on data corresponding to their class. Applied to several different classification tasks, we show that advocacy learning can lead to small improvements in classification accuracy over an identical supervised baseline. Through a series of follow-up experiments, we analyze when and how Advocates improve discriminative performance. Though it may seem counter-intuitive, a framework in which subnetworks are trained to competitively provide evidence in support of their class shows promise, performing as well as or better than standard approaches. This provides a foundation for further exploration into the effect of competition and class-conditional representations.

Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference    

tl;dr Finetuning after quantization matches or exceeds full-precision state-of-the-art networks at both 8- and 4-bit quantization.

To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. To this end, low-precision networks offer tremendous promise because both energy and area scale down quadratically with the reduction in precision. Here, for the first time, we demonstrate ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3, densenet-161, and VGG-16bn networks on the ImageNet classification benchmark that, at 8-bit precision exceed the accuracy of the full-precision baseline networks after one epoch of finetuning, thereby leveraging the availability of pretrained models. We also demonstrate for the first time ResNet-18, ResNet-34, and ResNet-50 4-bit models that match the accuracy of the full-precision baseline networks. Surprisingly, the weights of the low-precision networks are very close (in cosine similarity) to the weights of the corresponding baseline networks, making training from scratch unnecessary. We find that gradient noise due to quantization during training increases with reduced precision, and seek ways to overcome this noise. The number of iterations required by stochastic gradient descent to achieve a given training error is related to the square of (a) the distance of the initial solution from the final plus (b) the maximum variance of the gradient estimates. By drawing inspiration from this observation, we (a) reduce solution distance by starting with pretrained fp32 precision baseline networks and fine-tuning, and (b) combat noise introduced by quantizing weights and activations during training, by using larger batches along with matched learning rate annealing. Sensitivity analysis indicates that these techniques, coupled with proper activation function range calibration, offer a promising heuristic to discover low-precision networks, if they exist, close to fp32 precision baseline networks.


tl;dr "A GAN-style model to recover a solution of the Monge Problem"

Understanding and improving Generative Adversarial Networks (GAN) using notions from Optimal Transportation (OT) theory has been a successful area of study, originally established by the introduction of the Wasserstein GAN (WGAN). An increasing number of GANs incorporate OT for improving their discriminators, but that is so far the sole way for the two domains to cross-fertilize. We consolidate the bridge between GANs and OT with one model: W2GAN, where the discriminator approximates the second Wasserstein distance. This model exhibits a twofold connection: the discriminator implicitly computes an optimal map and the generator follows an optimal transport map during training. Perhaps surprisingly, we also provide empirical evidence that other GANs also approximately following the Optimal Transport.

Mean Replacement Pruning    

tl;dr Mean Replacement is an efficient method to improve the loss after pruning and Taylor approximation based scoring functions works better with absolute values.

Pruning units in a deep network can help speed up inference and training as wellas reduce the size of the model. We show thatbias propagationis a pruning tech-nique which consistently outperforms the common approach of merely removingunits, regardless of the architecture and the dataset. We also show how a sim-ple adaptation to an existing scoring function allows us to select the best units toprune. Finally, we show that the units selected by the best performing scoringfunctions are somewhat consistent over the course of training, implying the deadparts of the network appear during the stages of training.

Classifier-agnostic saliency map extraction    

tl;dr We propose a new saliency map extraction method which results in extracting higher quality maps.

Extracting saliency maps, which indicate parts of the image important to classification, requires many tricks to achieve satisfactory performance when using classifier-dependent methods. Instead, we propose classifier-agnostic saliency map extraction, which finds all parts of the image that any classifier could use, not just one given in advance. We observe that the proposed approach extracts higher quality saliency maps and outperforms existing weakly-supervised localization techniques, setting the new state of the art result on the ImageNet dataset.

Dynamic Channel Pruning: Feature Boosting and Suppression    

tl;dr We make convolutional layers run faster by dynamically boosting and suppressing channels in feature computation.

Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we exploit the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time. FBS introduces small auxiliary connections to existing convolutional layers. In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels. FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs. We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification. Experiments show that FBS can accelerate VGG-16 by 5x and improve the speed of ResNet-18 by 2x, both with less than 0.6% top-5 accuracy loss.

Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces    

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We introduce an approach to model surface properties governing bounces in everyday scenes. Our model learns end-to-end, starting from sensor inputs, to predict post-bounce trajectories and infer two underlying physical properties that govern bouncing - restitution and effective collision normals. Our model, Bounce and Learn, comprises two modules -- a Physics Inference Module (PIM) and a Visual Inference Module (VIM). VIM learns to infer physical parameters for locations in a scene given a single still image, while PIM learns to model physical interactions for the prediction task given physical parameters and observed pre-collision 3D trajectories. To achieve our results, we introduce the Bounce Dataset comprising 5K RGB-D videos of bouncing trajectories of a foam ball to probe surfaces of varying shapes and materials in everyday scenes including homes and offices. Our proposed model learns from our collected dataset of real-world bounces and is bootstrapped with additional information from simple physics simulations. We show qualitative and quantitative results on our newly collected dataset and outline open challenges for learning to model real-world bounces.

K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning    

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We introduce a novel method that enables parameter-efficient transfer and multitask learning. The basic approach is to allow a model patch - a small set of parameters - to specialize to each task, instead of fine-tuning the last layer or the entire network. For instance, we show that learning a set of scales and biases allows a network to learn a completely different embedding that could be used for different tasks (such as converting an SSD detection model into a 1000-class classification model while reusing 98% of parameters of the feature extractor). Similarly, we show that re-learning the existing low-parameter layers (such as depth-wise convolutions) also improves accuracy significantly. Our approach allows both simultaneous (multi-task) learning as well as sequential transfer learning wherein we adapt pretrained networks to solve new problems. For multi-task learning, despite using much fewer parameters than traditional logits-only fine-tuning, we match single-task-based performance.

Tangent-Normal Adversarial Regularization for Semi-supervised Learning    

tl;dr We propose a novel manifold regularization strategy based on adversarial training, which can significantly improve the performance of semi-supervised learning.

The ever-increasing size of modern datasets combined with the difficulty of obtaining label information has made semi-supervised learning of significant practical importance in modern machine learning applications. In comparison to supervised learning, the key difficulty in semi-supervised learning is how to make full use of the unlabeled data. In order to utilize manifold information provided by unlabeled data, we propose a novel regularization called the tangent-normal adversarial regularization, which is composed by two parts. The two parts complement with each other and jointly enforce the smoothness along two different directions that are crucial for semi-supervised learning. One is applied along the tangent space of the data manifold, aiming to enforce local invariance of the classifier on the manifold, while the other is performed on the normal space orthogonal to the tangent space, intending to impose robustness on the classifier against the noise causing the observed data deviating from the underlying data manifold. Both of the two regularizers are achieved by the strategy of virtual adversarial training. Our method has achieved state-of-the-art performance on semi-supervised learning tasks on both artificial dataset and practical datasets.

Towards Metamerism via Foveated Style Transfer    

tl;dr We introduce a novel feed-forward framework to generate visual metamers

The problem of visual metamerism is defined as finding a family of perceptually indistinguishable, yet physically different images. In this paper, we propose our NeuroFovea metamer model, a foveated generative model that is based on a mixture of peripheral representations and style transfer forward-pass algorithms. Our gradient-descent free model is parametrized by a foveated VGG19 encoder-decoder which allows us to encode images in high dimensional space and interpolate between the content and texture information with adaptive instance normalization anywhere in the visual field. Our contributions include: 1) A framework for computing metamers that resembles a noisy communication system via a foveated feed-forward encoder-decoder network – We observe that metamerism arises as a byproduct of noisy perturbations that partially lie in the perceptual null space; 2) A perceptual optimization scheme as a solution to the hyperparametric nature of our metamer model that requires tuning of the image-texture tradeoff coefficients everywhere in the visual field which are a consequence of internal noise; 3) An ABX psychophysical evaluation of our metamers where we also find that the rate of growth of the receptive fields in our model match V1 for reference metamers and V2 between synthesized samples. Our model also renders metamers at roughly a second, presenting a ×1000 speed-up compared to the previous work, which now allows for tractable data-driven metamer experiments.

(Unconstrained) Beam Search is Sensitive to Large Search Discrepancies    

tl;dr Analysis of the performance degradation in beam search and how constraining the the search can help avoiding it

Beam search is the most popular inference algorithm for decoding neural sequence models. Unlike greedy search, beam search allows for a non-greedy local decisions that can potentially lead to a sequence with a higher overall probability. However, previous work found that the performance of beam search tends to degrade with large beam widths. In this work, we perform an empirical study of the behavior of the beam search algorithm across three sequence synthesis tasks. We find that increasing the beam width leads to sequences that are disproportionately based on early and highly non-greedy decisions. These sequences typically include a very low probability token that is followed by a sequence of tokens with higher (conditional) probability leading to an overall higher probability sequence. However, as beam width increases, such sequences are more likely to have a lower evaluation score. Based on our empirical analysis we propose to constrain the beam search from taking highly non-greedy decisions early in the search. We evaluate two methods to constrain the search and show that constrained beam search effectively eliminates the problem of beam search degradation and in some cases even leads to higher evaluation scores. Our results generalize and improve upon previous observations on copies and training set predictions.

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator    

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Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection, and two-sample test. Furthermore, in the era of big data, designing divergence measure that is interpretable and can handle high-dimensional and complex data becomes extremely important. In this paper, we propose a post selection inference (PSI) framework for divergence measure, which can select a set of statistically significant features that discriminate two distributions. Specifically, we employ an additive variant of maximum mean discrepancy (MMD) for features and introduce a general hypothesis test for PSI. A novel MMD estimator using the incomplete U-statistics, which has an asymptotically normal distribution (under mild assumptions) and gives high detection power in PSI, is also proposed and analyzed theoretically. Through synthetic and real-world feature selection experiments, we show that the proposed framework can successfully detect statistically significant features. Last, we propose a sample selection framework for analyzing different members in the Generative Adversarial Networks (GANs) family.

Efficient Convolutional Neural Network Training with Direct Feedback Alignment    

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There were many algorithms to substitute the back-propagation (BP) in the deep neural network (DNN) training. However, they could not become popular because their training accuracy and the computational efficiency were worse than BP. One of them was direct feedback alignment (DFA), but it showed low training performance especially for the convolutional neural network (CNN). In this paper, we overcome the limitation of the DFA algorithm by combining with the conventional BP during the CNN training. To improve the training stability, we also suggest the feedback weight initialization method by analyzing the patterns of the fixed random matrices in the DFA. Finally, we propose the new training algorithm, binary direct feedback alignment (BDFA) to minimize the computational cost while maintaining the training accuracy compared with the DFA. In our experiments, we use the CIFAR-10 and CIFAR-100 dataset to simulate the CNN learning from the scratch and apply the BDFA to the online learning based object tracking application to examine the fully-connected layer (FC) fine-tuning task. Our proposed algorithms show better performance than conventional BP in both two different training tasks, but they have lighter computations for the error propagation.

BNN+: Improved Binary Network Training    

tl;dr The paper presents an improved training mechanism for obtaining binary networks with smaller accuracy drop compared to it's full precision

Deep neural networks (DNN) are widely used in many applications. However,their deployment on edge devices has been difficult because they are resource hungry. Binary networks (BNN) helps to alleviate the prohibitive resource requirements of DNN; where both activations and weights are limited to 1-bit. We propose an improved binary training method (BNN+) where this method is an improvement to the popular BNN training scheme, and helps reduce accuracy degradation compared to the full-precision counterpart. Our method is based on linear operations that are easily implementable into the binary training frame-work and we show experimental results on CIFAR-10 obtaining an accuracy of 86.5%, on AlexNet and 91.6%with VGG network. On ImageNet, our method also outperforms the traditional BNN and XNOR-net, by a margin of4% and 2% respectively

Normalization Gradients are Least-squares Residuals    

tl;dr Batch Normalization and its variants work by performing a least-squares fit during back-propagation, which zero-centers and decorrelates partial derivatives from normalized activations.

Batch Normalization (BN) and its variants have seen widespread adoption in the deep learning community because they improve the training of deep neural networks. Discussions of why this normalization works so well remain unsettled. We make explicit the relationship between ordinary least squares and partial derivatives computed when back-propagating through BN. We recast the back-propagation of BN as a least squares fit, which zero-centers and decorrelates partial derivatives from normalized activations. This view, which we term {\em gradient-least-squares}, is an extensible and arithmetically accurate description of BN. Our view offers a unified interpretation of BN and related work; we motivate, from a regression perspective, two improvements to BN, and evaluate on CIFAR-10.

Improving latent variable descriptiveness by modelling rather than ad-hoc factors    

tl;dr This paper introduces a novel generative modelling framework that avoids latent-variable collapse and clarifies the use of certain ad-hoc factors in training Variational Autoencoders.

Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variable. We discuss an alternative and general approach to latent variable modelling, based on an objective that encourages a perfect reconstruction by tying a stochastic autoencoder with a variational autoencoder (VAE). This ensures by design that the latent variable captures information about the observations, whilst retaining the ability to generate well. Interestingly, although our model is fundamentally different to a VAE, the lower bound attained is identical to the standard VAE bound but with the addition of a simple pre-factor; thus, providing a formal interpretation of the commonly used, ad-hoc pre-factors in training VAEs.

Learning Internal Dense But External Sparse Structures of Deep Neural Network    

tl;dr In this paper, we explore an internal dense yet external sparse network structure of deep neural networks and analyze its key properties.

Recent years have witnessed two seemingly opposite developments of deep convolutional neural networks (CNNs). On one hand, increasing the density of CNNs by adding cross-layer connections achieve higher accuracy. On the other hand, creating sparsity structures through regularization and pruning methods enjoys lower computational costs. In this paper, we bridge these two by proposing a new network structure with locally dense yet externally sparse connections. This new structure uses dense modules, as basic building blocks and then sparsely connects these modules via a novel algorithm during the training process. Experimental results demonstrate that the locally dense yet externally sparse structure could acquire competitive performance on benchmark tasks (CIFAR10, CIFAR100, and ImageNet) while keeping the network structure slim.

The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks    

tl;dr We introduce the NLC, a metric that is cheap to compute in the networks randomly initialized state and is highly predictive of generalization, at least in fully-connected networks.

For a long time, designing neural architectures that exhibit high performance was considered a dark art that required expert hand-tuning. One of the few well-known guidelines for architecture design is the avoidance of exploding or vanishing gradients. However, even this guideline has remained relatively vague and circumstantial, because there exists no well-defined, gradient-based metric that can be computed {\it before} training begins and can robustly predict the performance of the network {\it after} training is complete. We introduce what is, to the best of our knowledge, the first such metric: the nonlinearity coefficient (NLC). Via an extensive empirical study, we show that the NLC, computed in the network's randomly initialized state, is a powerful predictor of test error and that attaining a right-sized NLC is essential for attaining an optimal test error, at least in fully-connected feedforward networks. The NLC is also conceptually simple, cheap to compute, and is robust to a range of confounders and architectural design choices that comparable metrics are not necessarily robust to. Hence, we argue the NLC is an important tool for architecture search and design, as it can robustly predict poor training outcomes before training even begins.

Critical Learning Periods in Deep Networks    

tl;dr Sensory deficits in early training phases can lead to irreversible performance loss in both artificial and neuronal networks, suggesting information phenomena as the common cause, and point to the importance of the initial transient and forgetting.

Similar to humans and animals, deep artificial neural networks exhibit critical periods during which a temporary stimulus deficit can impair the development of a skill. The extent of the impairment depends on the onset and length of the deficit window, as in animal models, and on the size of the neural network. Deficits that do not affect low-level statistics, such as vertical flipping of the images, have no lasting effect on performance and can be overcome with further training. To better understand this phenomenon, we use the Fisher Information of the weights to measure the effective connectivity between layers of a network during training. Counterintuitively, information raises rapidly in the early phases of training, and then decreases, preventing redistribution of information resources in a phenomenon we refer to as a loss of "Information Plasticity". Our analysis suggests that the first few epochs are critical for the creation of strong connections that are optimal relative to the input data distribution. Once such strong connections are created, they do not appear to change during additional training. These findings suggest that the initial learning transient, under-scrutinized compared to asymptotic behavior, plays a key role in determining the outcome of the training process. Our findings, combined with recent theoretical results in the literature, also suggest that forgetting (decrease of information in the weights) is critical to achieving invariance and disentanglement in representation learning. Finally, critical periods are not restricted to biological systems, but can emerge naturally in learning systems, whether biological or artificial, due to fundamental constrains arising from learning dynamics and information processing.

Combining Global Sparse Gradients with Local Gradients    

tl;dr We improve gradient dropping (a technique of only exchanging large gradients on distributed training) by incorporating local gradients while doing a parameter update to reduce quality loss and further improve the training time.

Data-parallel neural network training is network-intensive, so gradient dropping was designed to exchange only large gradients. However, gradient dropping has been shown to slow convergence. We propose to improve convergence by having each node combine its locally computed gradient with the sparse global gradient exchanged over the network. We empirically confirm with machine translation tasks that gradient dropping with local gradients approaches convergence 48% faster than non-compressed multi-node training and 28% faster compared to vanilla gradient dropping. We also show that gradient dropping with a local gradient update does not reduce the model's final quality.

CEM-RL: Combining evolutionary and gradient-based methods for policy search    

tl;dr We propose a new combination of evolution strategy and deep reinforcement learning which takes the best of both worlds

Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search. The former is widely applicable and rather stable, but suffers from low sample efficiency. By contrast, the latter is more sample efficient, but the most sample efficient variants are also rather unstable and highly sensitive to hyper-parameter setting. So far, these families of methods have mostly been compared as competing tools. However, an emerging approach consists in combining them so as to get the best of both worlds. Two previously existing combinations use either a standard evolutionary algorithm or a goal exploration process together with the DDPG algorithm, a sample efficient off-policy deep RL algorithm. In this paper, we propose a different combination scheme using the simple cross-entropy method (CEM) and TD3, another off-policy deep RL algorithm which improves over DDPG. We evaluate the resulting algorithm, CEMRL, on a set of benchmarks classically used in deep RL. We show that \cemrl benefits from several advantages over its competitors and offers a satisfactory trade-off between performance and sample efficiency.

LIT: Block-wise Intermediate Representation Training for Model Compression    

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Knowledge distillation (KD) is a popular method for reducing the computational over- head of deep network inference, in which the output of a teacher model is used to train a smaller, faster student model. Hint training (i.e., FitNets) extends KD by regressing a student model’s intermediate representation to a teacher model’s intermediate representa- tion. In this work, we introduce bLock-wise Intermediate representation Training (LIT), a novel model compression technique that extends the use of intermediate represen- tations in deep network compression, outperforming KD and hint training. LIT has two key ideas: 1) LIT trains a student of the same width (but shallower depth) as the teacher by directly comparing the intermediate representations, and 2) LIT uses the intermediate representation from the previous block in the teacher model as an input to the current stu- dent block during training, avoiding unstable intermediate representations in the student network. We show that LIT provides substantial reductions in network depth without loss in accuracy — for example, LIT can compress a ResNeXt-110 to a ResNeXt-20 (5.5×) on CIFAR10 and a VDCNN-29 to a VDCNN-9 (3.2×) on Amazon Reviews without loss in accuracy, outperforming KD and hint training in network size at a given accuracy. We also show that applying LIT to identical student/teacher architectures increases the accuracy of the student model above the teacher model, outperforming the recently-proposed Born Again Networks procedure on ResNet, ResNeXt, and VDCNN. Finally, we show that LIT can effectively compress GAN generators, which are not supported in the KD framework because GANs output pixels as opposed to probabilities.

LanczosNet: Multi-Scale Deep Graph Convolutional Networks    

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We propose Lanczos network (LanczosNet) which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. Relying on the tridiagonal decomposition of the Lanczos algorithm, we not only efficiently exploit multi-scale information via fast approximated computation of matrix power but also design learnable spectral filters. Being fully differentiable, LanczosNet facilitates both graph kernel learning as well as learning node embeddings. We show the connection between our LanczosNet and graph based manifold learning, especially diffusion maps. We benchmark our model against $8$ recent deep graph networks on citation datasets and QM8 quantum chemistry dataset. Experimental results show that our model achieves the state-of-the-art performance in most tasks.

Where and when to look? Spatial-temporal attention for action recognition in videos    

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Inspired by the observation that humans are able to process videos efficiently by only paying attention when and where it is needed, we propose a novel spatial-temporal attention mechanism for video-based action recognition. For spatial attention, we learn a saliency mask to allow the model to focus on the most salient parts of the feature maps. For temporal attention, we employ a soft temporal attention mechanism to identify the most relevant frames from an input video. Further, we propose a set of regularizers that ensure that our attention mechanism attends to coherent regions in space and time. Our model is efficient, as it proposes a separable spatio-temporal mechanism for video attention, while being able to identify important parts of the video both spatially and temporally. We demonstrate the efficacy of our approach on three public video action recognition datasets. The proposed approach leads to state-of-the-art performance on all of them, including the new large-scale Moments in Time dataset. Furthermore, we quantitatively and qualitatively evaluate our model's ability to accurately localize discriminative regions spatially and critical frames temporally. This is despite our model only being trained with per video classification labels.

DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns    

tl;dr We introduce a new synthetic dataset for serial crystallography that can be used to train image classification models and explore computer vision and deep learning approaches to classify them.

Serial crystallography is the field of science that studies the structure and properties of crystals via diffraction patterns. In this paper, we introduce a new serial crystallography dataset generated through the use of a simulator; the synthetic images are labeled and they are both scalable and accurate. The resulting synthetic dataset is called DiffraNet, and it is composed of 25,000 512x512 grayscale labeled images. We explore several computer vision approaches for classification on DiffraNet such as standard feature extraction algorithms associated with Random Forests and Support Vector Machines but also an end-to-end CNN topology dubbed DeepFreak tailored to work on this new dataset. All implementations are publicly available and have been fine-tuned using off-the-shelf AutoML optimization tools for a fair comparison. Our best model achieves 98.5% accuracy. We believe that the DiffraNet dataset and its classification methods will have in the long term a positive impact in accelerating discoveries in many disciplines, including chemistry, geology, biology, materials science, metallurgy, and physics.

Hindsight policy gradients    

tl;dr We introduce the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended to policy gradient methods.

A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals. In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enable sample efficient learning. However, reinforcement learning agents have only recently been endowed with such capacity for hindsight. In this paper, we demonstrate how hindsight can be introduced to policy gradient methods, generalizing this idea to a broad class of successful algorithms. Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency.

Decoupled Weight Decay Regularization    

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L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it ``weight decay'' in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments will be available after the review process.

No Training Required: Exploring Random Encoders for Sentence Classification    

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We explore various methods for computing sentence representations from pre-trained word embeddings without any training, i.e., using nothing but random parameterizations. Our aim is to put sentence embeddings on more solid footing by 1) looking at how much modern sentence embeddings gain over random methods---as it turns out, surprisingly little; and by 2) providing the field with more appropriate baselines going forward---which are, as it turns out, quite strong. We also make important observations about proper experimental protocol for sentence classification evaluation, together with recommendations for future research.

DecayNet: A Study on the Cell States of Long Short Term Memories    

tl;dr We present a LSTM reformulation with a monotonically decreasing forget gate to increase LSTM interpretability and modelling power without introducing new learnable parameters.

It is unclear whether the extensively applied long short term memory (LSTM) is an optimised architecture for recurrent neural networks. Its complicated design and opaque mechanics make the network hard to analyse and non-immediately clear for its utilities in real-world data. This paper studies LSTMs as systems of difference equations, and takes a theoretical mathematical approach to study consecutive transitions in network variables. Our study shows that the cell state propagation is predominantly controlled by the forget gate. Based on these mathematical insights, we introduce the DecayNet reformulation to calibrate cell state dynamics with a monotonically decreasing forget gate. The reformulation increases LSTM modelling power without the need for introducing new learnable parameters; and also yields more consistent results.

Function Space Particle Optimization for Bayesian Neural Networks    

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While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To address this issue, several highly flexible and scalable variational inference procedures based on the idea of particle optimization have been proposed. These methods directly optimize a set of particles to approximate the target posterior. However, their application to BNNs often yields sub-optimal performance, as such methods have a particular failure mode on over-parameterized models. In this paper, we propose to solve this issue by performing particle optimization directly in the space of regression functions. We demonstrate through extensive experiments that our method successfully overcomes this issue, and outperforms strong baselines in a variety of tasks including prediction, defense against adversarial examples, and reinforcement learning.

Spherical CNNs on Unstructured Grids    

tl;dr We present a new CNN kernel for unstructured grids for spherical signals, and show significant accuracy and parameter efficiency gain on tasks such as 3D classfication and omnidirectional image segmentation.

We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals. To this end, we replace conventional convolution kernels with linear combinations of differential operators that are weighted by learnable parameters. Differential operators can be efficiently estimated on unstructured grids using one-ring neighbors, and learnable parameters can be optimized through standard back-propagation. As a result, we obtain extremely efficient neural networks that match or outperform state-of-the-art network architectures in terms of performance but with a significantly lower number of network parameters. We evaluate our algorithm in an extensive series of experiments on a variety of computer vision and climate science tasks, including shape classification, climate pattern segmentation, and omnidirectional image semantic segmentation. Overall, we present (1) a novel CNN approach on unstructured grids using parameterized differential operators for spherical signals, and (2) we show that our unique kernel parameterization allows our model to achieve the same or higher accuracy with significantly fewer network parameters.

Large-scale classification of structured objects using a CRF with deep class embedding    

tl;dr We present a technique for ultrafine-grained, large-scale structured classification, based on CRF modeling with factorized pairwise potentials, learned as neighboring class embedding in a whitened space.

This paper presents a novel deep learning architecture for classifying structured objects in ultrafine-grained datasets, where classes may not be clearly distinguishable by their appearance but rather by their context. We model sequences of images as linear-chain CRFs, and jointly learn the parameters from both local-visual features and neighboring class information. The visual features are learned by convolutional layers, whereas class-structure information is reparametrized by factorizing the CRF pairwise potential matrix. This forms a context-based semantic similarity space, learned alongside the visual similarities, and dramatically increases the learning capacity of contextual information. This new parametrization, however, forms a highly nonlinear objective function which is challenging to optimize. To overcome this, we develop a novel surrogate likelihood which allows for a local likelihood approximation of the original CRF with integrated batch-normalization. This model overcomes the difficulties of existing CRF methods to learn the contextual relationships thoroughly when there is a large number of classes and the data is sparse. The performance of the proposed method is illustrated on a huge dataset that contains images of retail-store product displays, and shows significantly improved results compared to linear CRF parametrization, unnormalized likelihood optimization, and RNN modeling.

Mol-CycleGAN - a generative model for molecular optimization    

tl;dr We introduce Mol-CycleGAN - a new generative model for optimization of molecules to augment drug design.

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN -- a CycleGAN-based model that generates optimized compounds with a chemical scaffold of interest. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.

Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks    

tl;dr We present an analytical framework to determine accumulation bit-width requirements in all three deep learning training GEMMs and verify the validity and tightness of our method via benchmarking experiments.

Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices. This imposes an upper-bound on the reduction of complexity of multiply-accumulate units. We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training. Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation. We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet. In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline. We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds. Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.

Learning deep representations by mutual information estimation and maximization    

tl;dr We learn deep representation by maximizing mutual information, leveraging structure in the objective, and are able to compute with fully supervised classifiers with comparable architectures

In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a representation's suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and competes with fully-supervised learning on several classification tasks. DIM opens new avenues for unsupervised learning of representations and is an important step towards the flexible formulation of representation-learning objectives for specific end-goals.

Generative Question Answering: Learning to Answer the Whole Question    

tl;dr Question answering models that model the joint distribution of questions and answers can learn more than discriminative models

Discriminative question answering models can overfit to superficial biases in datasets, because their loss function saturates when any clue makes the answer likely. We introduce generative models of the joint distribution of questions and answers, which are trained to explain the whole question, not just to answer it.Our question answering (QA) model is implemented by learning a prior over answers, and a conditional language model to generate the question given the answer—allowing scalable and interpretable many-hop reasoning as the question is generated word-by-word. Our model achieves competitive performance with specialised discriminative models on the SQUAD and CLEVR benchmarks, indicating that it is a more general architecture for language understanding and reasoning than previous work. The model greatly improves generalisation both from biased training data and to adversarial testing data, achieving a new state-of-the-art on ADVERSARIAL SQUAD. We will release our code.

Countdown Regression: Sharp and Calibrated Survival Predictions    

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Personalized probabilistic forecasts of time to event (such as mortality) can be crucial in decision making, especially in the clinical setting. Inspired by ideas from the meteorology literature, we approach this problem through the paradigm of maximizing sharpness of prediction distributions, subject to calibration. In regression problems, it has been shown that optimizing the continuous ranked probability score (CRPS) instead of maximum likelihood leads to sharper prediction distributions while maintaining calibration. We introduce the Survival-CRPS, a generalization of the CRPS to the time to event setting, and present right-censored and interval-censored variants. To holistically evaluate the quality of predicted distributions over time to event, we present the scale agnostic Survival-AUPRC evaluation metric, an analog to area under the precision-recall curve. We apply these ideas by building a recurrent neural network for mortality prediction, using an Electronic Health Record dataset covering millions of patients. We demonstrate significant benefits in models trained by the Survival-CRPS objective instead of maximum likelihood.

An Information-Theoretic Metric of Transferability for Task Transfer Learning    

tl;dr We present a provable and easily-computable evaluation function that estimates the performance of transferred representations from one learning task to another in task transfer learning.

An important question in task transfer learning is to determine task transferability, i.e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task. Typically, transferability is either measured experimentally or inferred through task relatedness, which is often defined without a clear operational meaning. In this paper, we present a novel metric, H-score, an easily-computable evaluation function that estimates the performance of transferred representations from one task to another in classification problems. Inspired by a principled information theoretic approach, H-score has a direct connection to the asymptotic error probability of the decision function based on the transferred feature. This formulation of transferability can further be used to select a suitable set of source tasks in task transfer learning problems or to devise efficient transfer learning policies. Experiments using both synthetic and real image data show that not only our formulation of transferability is meaningful in practice, but also it can generalize to inference problems beyond classification, such as recognition tasks for 3D indoor-scene understanding.

Diversity and Depth in Per-Example Routing Models    

tl;dr Per-example routing models benefit from architectural diversity, but still struggle to scale to a large number of routing decisions.

Routing models, a form of conditional computation where examples are routed through a subset of components in a larger network, have shown promising results in recent works. Surprisingly, routing models to date have lacked important properties, such as architectural diversity and large numbers of routing decisions. Both architectural diversity and routing depth can increase the representational power of a routing network. In this work, we address both of these deficiencies. We discuss the significance of architectural diversity in routing models, and explain the tradeoffs between capacity and optimization when increasing routing depth. In our experiments, we find that adding architectural diversity to routing models significantly improves performance, cutting the error rates of a strong baseline by 35% on an Omniglot setup. However, when scaling up routing depth, we find that modern routing techniques struggle with optimization. We conclude by discussing both the positive and negative results, and suggest directions for future research.

Causal importance of orientation selectivity for generalization in image recognition    

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Although both our brain and deep neural networks (DNNs) can perform high-level sensory-perception tasks such as image or speech recognition, the inner mechanism of these hierarchical information-processing systems is poorly understood in both neuroscience and machine learning. Recently, Morcos et al. (2018) examined the effect of class-selective units in DNNs, i.e., units with high-level selectivity, on network generalization, concluding that hidden units that are selectively activated by specific input patterns may harm the network's performance. In this study, we revisit their hypothesis, considering units with selectivity for lower-level features, and argue that selective units are not always harmful to the network performance. Specifically, by using DNNs trained for image classification, we analyzed the orientation selectivity of individual units. Orientation selectivity is a low-level selectivity widely studied in visual neuroscience, in which, when images of bars with several orientations are presented to the eye, many neurons in the visual cortex respond selectively to a specific orientation. We found that orientation-selective units exist in both lower and higher layers of DNNs, as in our brain. In particular, units in the lower layers become more orientation-selective as the generalization performance improves during the course of training of the DNNs. Consistently, networks that generalize better are more orientation-selective in the lower layers. We finally reveal that ablating these selective units in the lower layers substantially degrades the generalization performance. These results suggest to the machine-learning community that, contrary to the triviality of units with high-level selectivity, lower-layer units with selectivity for low-level features are indispensable for generalization, and for neuroscientists, orientation selectivity does play a causally important role in object recognition.

Predictive Uncertainty through Quantization    

tl;dr A novel tractable and flexible variational distribution through quantization of latent variables, applied to the deep variational information bottleneck objective for improved uncertainty.

High-risk domains require reliable confidence estimates from predictive models. Deep latent variable models provide these, but suffer from the rigid variational distributions used for tractable inference, which err on the side of overconfidence. We propose Stochastic Quantized Activation Distributions (SQUAD), which imposes a flexible yet tractable distribution over discretized latent variables. The proposed method is scalable, self-normalizing and sample efficient. We demonstrate that the model fully utilizes the flexible distribution, learns interesting non-linearities, and provides predictive uncertainty of competitive quality.

Object-Oriented Model Learning through Multi-Level Abstraction    

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Object-based approaches for learning action-conditioned dynamics has demonstrated promise of strong generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), for learning object-based dynamics models from raw visual observations. MAOP employs three-level learning archicture that enables efficient dynamics learning for complex environments with a dynamic background. We also design a spatial-temporal relational reasoning mechanism to support instance-level dynamics learning and handle partial observability. Empirical results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments that have multiple controllable and uncontrollable dynamic objects and different static object layouts. In addition, MAOP learns semantically and visually interpretable disentangled representations.

Laplacian Smoothing Gradient Descent    

tl;dr We proposal a simple surrogate for gradient descent to improve training of deep neural nets and other optimization problems.

We propose a class of very simple modifications of gradient descent and stochastic gradient descent. We show that when applied to a large variety of machine learning problems, ranging from softmax regression to deep neural nets, the proposed surrogates can dramatically reduce the variance and improve the generalization accuracy. The methods only involve multiplying the usual (stochastic) gradient by the inverse of a positive definitive matrix coming from the discrete Laplacian or high order generalizations. The theory of Hamilton-Jacobi partial differential equations demonstrates that the new algorithm is almost the same as doing gradient descent on a new function which (i) has the same global minima as the original function and (ii) is “more convex”. We show that optimization algorithms with these surrogates converge uniformly in the discrete Sobolev H^p_\sigma sense and reduce the optimality gap for convex optimization problems. We implement our algorithm into both PyTorch and Tensorflow platforms which only involves changing of a few lines of code. The code will be available on Github.

Generative replay with feedback connections as a general strategy for continual learning    

tl;dr A structured comparison of recent methods for continual learning that turns into an argument for and extension of generative replay.

Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning problematic. Recently, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly compare their performance. To enable more meaningful comparisons, we identified three distinct continual learning scenarios based on whether task identity is known and, if it is not, whether it needs to be inferred. Performing the split and permuted MNIST task protocols according to each of these scenarios, we found that regularization-based approaches (e.g., elastic weight consolidation) failed when task identity needed to be inferred. In contrast, generative replay combined with distillation (i.e., using class probabilities as ''soft targets'') achieved superior performance in all three scenarios. In addition, we reduced the computational cost of generative replay by integrating the generative model into the main model by equipping it with generative feedback connections. This Replay-through-Feedback approach substantially shortened training time with no or negligible loss in performance. We believe this to be an important first step towards making the powerful technique of generative replay scalable to real-world continual learning applications.

Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution    

tl;dr We propose a method for efficient Multi-Objective Neural Architecture Search based on Lamarckian inheritance and evolutionary algorithms.

Architecture search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1) the neural architectures found are solely optimized for high predictive performance, without penalizing excessive resource consumption; (2)most architecture search methods require vast computational resources. We address the first shortcoming by proposing LEMONADE, an evolutionary algorithm for multi-objective architecture search that allows approximating the Pareto-front of architectures under multiple objectives, such as predictive performance and number of parameters, in a single run of the method. We address the second shortcoming by proposing a Lamarckian inheritance mechanism for LEMONADE which generates children networks that are warmstarted with the predictive performance of their trained parents. This is accomplished by using (approximate) network morphism operators for generating children. The combination of these two contributions allows finding models that are on par or even outperform different-sized NASNets, MobileNets, MobileNets V2 and Wide Residual Networks on CIFAR-10 and ImageNet64x64 within only one week on eight GPUs, which is about 20-40x less compute power than previous architecture search methods that yield state-of-the-art performance.

Adversarial Audio Synthesis    

tl;dr Learning to synthesize raw waveform audio with GANs

While Generative Adversarial Networks (GANs) have seen wide success at the problem of synthesizing realistic images, they have seen little application to audio generation. Unlike for images, a barrier to success is that the best discriminative representations for audio tend to be non-invertible, and thus cannot be used to synthesize listenable outputs. In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. Our experiments demonstrate that WaveGAN can produce intelligible words from a small vocabulary of speech, and can also synthesize audio from other domains such as drums, bird vocalizations, and piano. Qualitatively, we find that human judges prefer the sound quality of generated examples from WaveGAN over those from a method which naïvely apply GANs on image-like audio feature representations.

Wasserstein proximal of GANs    

tl;dr We propose the Wasserstein proximal method for training GANs.

We introduce a new method for training GANs by applying the Wasserstein-2 metric proximal on the generators. The approach is based on the gradient operator induced by optimal transport, which connects the geometry of sample space and parameter space in implicit deep generative models. From this theory, we obtain an easy-to-implement regularizer for the parameter updates. Our experiments demonstrate that this method improves the speed and stability in training GANs in terms of wall-clock time and Fr\'echet Inception Distance (FID) learning curves.

Learning Preconditioners on Lie Groups    

tl;dr We propose a new framework for preconditioner learning, derive new forms of preconditioners and learning methods, and reveal the relationship to methods like RMSProp, Adam, Adagrad, ESGD, KFAC, batch normalization, etc.

We study two types of preconditioners and preconditioned stochastic gradient descent (SGD) methods in a unified framework. We call the first one the Newton type due to its close relationship to Newton method, and the second one the Fisher type as its preconditioner is closely related to the inverse of Fisher information matrix. Both preconditioners can be derived from one framework, and efficiently learned on any matrix Lie groups designated by the user using natural or relative gradient descent. Many existing preconditioners and methods are special cases of either the Newton type or the Fisher type ones. Experimental results on relatively large scale machine learning problems are reported for performance study.

Variational Domain Adaptation    

tl;dr This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference

This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference. Un- like the existing methods on domain transfer through deep generative models, such as CycleGAN (Zhu et al., 2017a) and StarGAN (Choi et al., 2017), the variational domain adaptation has three advantages. Firstly, the samples from the target are not required. Instead, the framework requries one known source as a prior p(x) and binary discriminators, p(D i |x), discriminating the target domain D i from oth- ers. Consequently, the framework regards a target as a posterior that can be ex- plicitly formulated through the Bayesian inference, p(x|D i ) ∝ p(D i |x)p(x), as exhibited by a further proposed model of multi-domain variational autoencoder (MD-VAE). Secondly, the framework is scablable to large-scale domains. MD- VAE sophisticatedly puts together all the domains as well as the samples drawn from the prior into normal distributions in the same latent space as embeddings. The model enables us to expand the method to uncountable infinite domains such as continuous domains as well as interpolation. Thirdly, with MD-VAE, no need to search hyperparameter anymore. Although several domain transfer based on adversarial learning need sophisticated automatic/manual hyperparameter search, MD-VAE fast converges with less tuning because it has only one trainable matrix in addition to VAE. In the experiment part, we experimentally demonstrate the benefit with multi-domain image generation task on CelebA and facial image data that are obtained based on evaluation by 60 users, the model generates an ideal image that can be evaluated to be good by multiple users. Additionally, our exper- imental result exhibits that our model outperforms several state-of-the-art models.

PA-GAN: Improving GAN Training by Progressive Augmentation    

tl;dr We introduce a new technique - progressive augmentation of GANs (PA-GAN) - that helps to improve the overall stability of GAN training.

Despite recent progress, Generative Adversarial Networks (GANs) still suffer from training instability, requiring careful consideration of architecture design choices and hyper-parameter tuning. The reason for this fragile training behaviour is partially due to the discriminator performing well very quickly; its loss converges to zero, providing no reliable backpropagation signal to the generator. In this work we introduce a new technique - progressive augmentation of GANs (PA-GAN) - that helps to overcome this fundamental limitation and improve the overall stability of GAN training. The key idea is to gradually increase the task difficulty of the discriminator by progressively augmenting its input space, thus enabling continuous learning of the generator. We show that the proposed progressive augmentation preserves the original GAN objective, does not bias the optimality of the discriminator and encourages the healthy competition between the generator and discriminator, leading to a better-performing generator. We experimentally demonstrate the effectiveness of the proposed approach on multiple benchmarks (MNIST, Fashion-MNIST, CIFAR10, CELEBA) for the image generation task.

What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation    

tl;dr This study highlights a key difference between human vision and CNNs: while object recognition in humans relies on analysing shape, CNNs do not have such a shape-bias.

Convolutional neural networks (CNNs) were inspired by human vision and, in some settings, achieve a performance comparable to human object recognition. This has lead to the speculation that both systems use similar mechanisms to perform recognition. In this study, we conducted a series of simulations that indicate that there is a fundamental difference between human vision and CNNs: while object recognition in humans relies on analysing shape, CNNs do not have such a shape-bias. We teased apart the type of features selected by the model by modifying the CIFAR-10 dataset so that, in addition to containing objects with shape, the images concurrently contained non-shape features, such as a noise-like mask. When trained on these modified set of images, the model did not show any bias towards selecting shapes as features. Instead it relied on whichever feature allowed it to perform the best prediction -- even when this feature was a noise-like mask or a single predictive pixel amongst 50176 pixels. We also found that regularisation methods, such as batch normalisation or Dropout, did not change this behaviour and neither did past or concurrent experience with images from other datasets.

Adaptive Posterior Learning    

tl;dr We introduce a model which generalizes quickly from few observations by storing surprising information and attending over the most relevant data at each time point.

The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall. We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint. In addition, its memory compression allows it to scale to thousands of unknown labels. Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification. In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.

RoC-GAN: Robust Conditional GAN    

tl;dr We introduce a new type of conditional GAN, which aims to leverage structure in the target space of the generator. We augment the generator with a new, unsupervised pathway to learn the target structure.

Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision. The major focus so far has been on performance improvement, while there has been little effort in making cGAN more robust to noise or leveraging structure in the output space of the model. The end-to-end regression (of the generator) might lead to arbitrarily large errors in the output, which is unsuitable for the application of such networks to real-world systems. In this work, we introduce a novel conditional GAN model, called RoC-GAN, which adds implicit constraints to address the issue. Our model augments the generator with an unsupervised pathway, which promotes the outputs of the generator to span the target manifold even in the presence of large amounts of noise. We prove that RoC-GAN share similar theoretical properties as GAN and experimentally verify that our model outperforms existing state-of-the-art cGAN architectures by a large margin in a variety of domains including images from natural scenes and faces.

Learning Protein Structure with a Differentiable Simulator    

tl;dr We use an unrolled simulator of a neural energy function as an end-to-end differentiable model of protein structure and show it can hierarchically generalize to unseen fold types.

The Boltzmann distribution is a natural model for many systems, from brains to materials and biomolecules, but is often of limited utility for fitting data because Monte Carlo algorithms are unable simulate it in available time. This gap between the expressive capabilities and sampling practicalities of energy-based models is exemplified by the protein folding problem, since energy landscapes underlie contemporary knowledge of protein biophysics but computer simulations are still unable to fold all but the smallest proteins from first-principles. In this work we bridge the gap between the expressive capacity of energy functions and the practical capabilities of their simulators by using an unrolled Monte Carlo simulation as a model for data. We compose a neural energy function with a novel and efficient simulator based on Langevin dynamics to build an end-to-end-differentiable model of atomic protein structure given amino acid sequence information. We introduce techniques for stabilizing backpropagation under long roll-outs and demonstrate the model's capacity to make multimodal predictions and to generalize to unobserved protein fold types when trained on a large corpus of protein structures.

IEA: Inner Ensemble Average within a convolutional neural network    

tl;dr We inner ensemble the features of a convolutional neural layer, it increases the network accuracy and generates distinct features.

Ensemble learning is a method of combining multiple trained models to improve the model accuracy. We introduce the usage of such methods, specifically ensemble average inside Convolutional Neural Networks (CNNs) architectures. By Inner Average Ensemble (IEA) of multiple convolutional neural layers (CNLs) replacing the single CNL inside the CNN architecture, the accuracy of the CNN increased. A visual and a similarity score analysis of the features generated from IEA explains why it boosts the model performance. Empirical results using different benchmarking datasets and well-known deep model architectures shows that IEA outperforms the ordinary CNL used in CNNs.

Architecture Compression    

tl;dr Novel gradient descent approach to perform model compression in architecture space

In this paper we propose a novel approach to model compression termed Architecture Compression. Instead of operating on the weight or filter space of the network like classical model compression methods, our approach operates on the architecture space. A 1-D CNN encoder/decoder is trained to learn a mapping from discrete architecture space to a continuous embedding and back. Additionally, this embedding is jointly trained to regress accuracy and parameter count in order to incorporate information about the architecture's effectiveness on the dataset. During the compression phase, we first encode the network and then perform gradient descent in continuous space to optimize a compression objective function that maximizes accuracy and minimizes parameter count. The final continuous feature is then mapped to a discrete architecture using the decoder. We demonstrate the merits of this approach on visual recognition tasks such as CIFAR-10/100, FMNIST and SVHN and achieve a greater than 20x compression on CIFAR-10.

Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs    

tl;dr A statistical approach to compute sample likelihoods in Generative Adversarial Networks

Building on the success of deep learning, two modern approaches to learn a probability model of the observed data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs). VAEs consider an explicit probability model for the data and compute a generative distribution by maximizing a variational lower-bound on the log-likelihood function. GANs, however, compute a generative model by minimizing a distance between observed and generated probability distributions without considering an explicit model for the observed data. The lack of having explicit probability models in GANs prohibits computation of sample likelihoods in their frameworks and limits their use in statistical inference problems. In this work, we show that an optimal transport GAN with the entropy regularization can be viewed as a generative model that maximizes a lower-bound on sample likelihoods, an approach that VAEs are based on. In particular, our proof constructs an explicit probability model for GANs that can be used to compute likelihood statistics within GAN’s framework. Our numerical results on several datasets demonstrate consistent trends with the proposed theory.

Sinkhorn AutoEncoders    

No tl;dr =[

Optimal Transport offers an alternative to maximum likelihood for learning generative autoencoding models. We show how this principle dictates the minimization of the Wasserstein distance between the encoder aggregated posterior and the prior, plus a reconstruction error. We prove that in the non-parametric limit the autoencoder generates the data distribution if and only if the two distributions match exactly, and that the optimum can be obtained by deterministic autoencoders. We then introduce the Sinkhorn AutoEncoder (SAE), which casts the problem into Optimal Transport on the latent space. The resulting Wasserstein distance is minimized by backpropagating through the Sinkhorn algorithm. SAE models the aggregated posterior as an implicit distribution and therefore does not need a reparameterization trick for gradients estimation. Moreover, it requires virtually no adaptation to different prior distributions. We demonstrate its flexibility by considering models with hyperspherical and Dirichlet priors, as well as a simple case of probabilistic programming. SAE matches or outperforms other autoencoding models in visual quality and FID scores.

A unified theory of adaptive stochastic gradient descent as Bayesian filtering    

tl;dr We formulated SGD as a Bayesian filtering problem, and show that this gives rise to RMSprop, Adam, AdamW, NAG and other features of state-of-the-art adaptive methods

We formulate stochastic gradient descent (SGD) as a Bayesian filtering problem. Inference in the Bayesian setting naturally gives rise to BRMSprop and BAdam: Bayesian variants of RMSprop and Adam. Remarkably, the Bayesian approach recovers many features of state-of-the-art adaptive SGD methods, including amoungst others root-mean-square normalization, Nesterov acceleration and AdamW. As such, the Bayesian approach provides one explanation for the empirical effectiveness of state-of-the-art adaptive SGD algorithms. Empirically comparing BRMSprop and BAdam with naive RMSprop and Adam on MNIST, we find that Bayesian methods have the potential to considerably reduce test loss and classification error.

The role of over-parametrization in generalization of neural networks    

tl;dr We suggest a generalization bound that could potentially explain the improvement in generalization with over-parametrization.

Despite existing work on ensuring generalization of neural networks in terms of scale sensitive complexity measures, such as norms, margin and sharpness, these complexity measures do not offer an explanation of why neural networks generalize better with over-parametrization. In this work we suggest a novel complexity measure based on unit-wise capacities resulting in a tighter generalization bound for two layer ReLU networks. Our capacity bound correlates with the behavior of test error with increasing network sizes, and could potentially explain the improvement in generalization with over-parametrization. We further present a matching lower bound for the Rademacher complexity that improves over previous capacity lower bounds for neural networks.

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.    

tl;dr ImageNet-trained CNNs are biased towards object texture (instead of shape like humans). Overcoming this bias (using a novel data augmentation) yields improved detection performance and previously unseen robustness to image distortions.

Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies hint to a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on our novel Stylized-ImageNet dataset. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation.

Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms    

No tl;dr =[

Deep learning on an edge device requires energy efficient operation due to ever diminishing power budget. Intentional low quality data during the data acquisition for longer battery life, and natural noise from the low cost sensor degrade the quality of target output which hinders adoption of deep learning on an edge device. To overcome these problems, we propose simple yet efficient mixture of pre-processing experts (MoPE) model to handle various image distortions including low resolution and noisy images. We also propose to use adversarially trained auto encoder as a pre-processing expert for the noisy images. We evaluate our proposed method for various machine learning tasks including object detection on MS-COCO 2014 dataset, multiple object tracking problem on MOT-Challenge dataset, and human activity recognition on UCF 101 dataset. Experimental results show that the proposed method achieves better detection, tracking and activity recognition accuracies under noise without sacrificing accuracies for the clean images. The overheads of our proposed MoPE are 0.67% and 0.17% in terms of memory and computation compared to the baseline object detection network.

Meta-Learning with Individualized Feature Space for Few-Shot Classification    

No tl;dr =[

Meta-learning provides a promising learning framework to address few-shot classification tasks. In existing meta-learning methods, the meta-learner is designed to learn about model optimization, parameter initialization, or similarity metric. Differently, in this paper, we propose to learn how to create an individualized feature embedding specific to a given query image for better classifying, i.e., given a query image, a specific feature embedding tailored for its characteristics is created accordingly, leading to an individualized feature space in which the query image can be more accurately classified.  Specifically, we introduce a kernel generator as meta-learner to learn to construct feature embedding for query images. The kernel generator acquires meta-knowledge of generating adequate convolutional kernels for different query images during training, which can generalize to unseen categories without fine-tuning. In two standard few-shot classification data sets, i.e. Omniglot, and \emph{mini}ImageNet, our method shows highly competitive performance.

Capsules Graph Neural Network    

tl;dr Inspired by CapsNet, we propose a novel architecture for graph embeddings on the basis of node features extracted from GNN.

The high-quality node embeddings learned by GNN have been applied to a wide range of node-based graph structured applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNN to generate graph embeddings, the scalar node representation typically used in GNN may not suffice to preserve the node/graph relationships, resulting in sub-optimal graph embeddings. Inspired by the Capsule Neural Network (CapsNet), we propose the Capsules Graph Neural Network (CapsGNN), which adopts the concept of capsules to address the weakness in existing GNN-based graph embeddings algorithms. By extracting node features in the form of capsules, routing mechanism can be utilized to capture important statistic information at the graph level. As a result, our model generates multiple embeddings for each graph to capture significant graph properties from different aspects. The attention module incorporated in CapsGNN is used to tackle graphs with various sizes which also enables the model to focus on critical parts of the graphs. Our extensive evaluations with 9 graph-structured datasets demonstrate that CapsGNN has a high potential for large graph data analysis and powerful capability in capturing macroscopic properties of the whole graph. It outperforms other SOTA techniques on several graph classification tasks.

Found by NEMO: Unsupervised Object Detection from Negative Examples and Motion    

tl;dr Learning to detect objects without image labels from 3 minutes of video

This paper introduces NEMO, an approach to unsupervised object detection that uses motion---instead of image labels---as a cue to learn object detection. To discriminate between motion of the target object and other changes in the image, it relies on negative examples that show the scene without the object. The required data can be collected very easily by recording two short videos, a positive one showing the object in motion and a negative one showing the scene without the object. Without any additional form of pretraining or supervision and despite of occlusions, distractions, camera motion, and adverse lighting, those videos are sufficient to learn object detectors that can be applied to new videos and even generalize to unseen scenes and camera angles. In a baseline comparison, unsupervised object detection outperforms off-the shelf template matching and tracking approaches that are given an initial bounding box of the object. The learned object representations are also shown to be accurate enough to capture the relevant information from manipulation task demonstrations, which makes them applicable to learning from demonstration in robotics. An example of object detection that was learned from 3 minutes of video can be found here:

On Learning Heteroscedastic Noise Models within Differentiable Bayes Filters    

tl;dr We evaluate learning heteroscedastic noise models within different Differentiable Bayes Filters

In many robotic applications, it is crucial to maintain a belief about the state of a system, like the location of a robot or the pose of an object. These state estimates serve as input for planning and decision making and provide feedback during task execution. Recursive Bayesian Filtering algorithms address the state estimation problem, but they require a model of the process dynamics and the sensory observations as well as noise estimates that quantify the accuracy of these models. Recently, multiple works have demonstrated that the process and sensor models can be learned by end-to-end training through differentiable versions of Recursive Filtering methods. However, even if the predictive models are known, finding suitable noise models remains challenging. Therefore, many practical applications rely on very simplistic noise models. Our hypothesis is that end-to-end training through differentiable Bayesian Filters enables us to learn more complex heteroscedastic noise models for the system dynamics. We evaluate learning such models with different types of filtering algorithms and on two different robotic tasks. Our experiments show that especially for sampling-based filters like the Particle Filter, learning heteroscedastic noise models can drastically improve the tracking performance in comparison to using constant noise models.

Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking    

No tl;dr =[

Deep Neural Networks (DNN) are increasingly deployed in highly energy-constrained environments such as autonomous drones and wearable devices while at the same time must operate in real-time. Therefore, reducing the energy consumption has become a major design consideration in DNN training. This paper proposes the first end-to-end DNN training framework that provides quantitative energy consumption guarantees via weighted sparse projection and input masking. The key idea is to formulate the DNN training as an optimization problem in which the energy budget imposes a previously unconsidered optimization constraint. We integrate the quantitative DNN energy estimation into the DNN training process to assist the constrained optimization. We prove that an approximate algorithm can be used to efficiently solve the optimization problem. Compared to the best prior energy-saving techniques, our framework trains DNNs that provide higher accuracies under same or lower energy budgets.

Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer    

tl;dr An image to image translation method which adds to one image the content of another thereby creating a new image.

We study the problem of learning to map, in an unsupervised way, between domains A and B, such that the samples b in B contain all the information that exists in samples $\va\in A$ and some additional information. For example, ignoring occlusions, B can be people with glasses, A people without, and the glasses, would be the added information. When mapping a sample a from the first domain to the other domain, the missing information is replicated from an independent reference sample b in B. Thus, in the above example, we can create, for every person without glasses a version with the glasses observed in any face image. Our solution employs a single two-pathway encoder and a single decoder for both domains. The common part of the two domains and the separate part are encoded as two vectors, and the separate part is fixed at zero for domain A. The loss terms are minimal and involve reconstruction losses for the two domains and a domain confusion term. Our analysis shows that under mild assumptions, this architecture, which is much simpler than the literature guided-translation methods, is enough to ensure disentanglement between the two domains. We present convincing results in a few visual domains, such as no-glasses to glasses, adding facial hair based on a reference image, etc.

SGD Converges to Global Minimum in Deep Learning via Star-convex Path    

No tl;dr =[

Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how and why SGD can train these complex networks towards a global minimum. In this study, we establish the convergence of SGD to a global minimum for nonconvex optimization problems that are commonly encountered in neural network training. Our argument exploits the following two important properties: 1) the training loss can achieve zero value (approximately), which has been widely observed in deep learning; 2) SGD follows a star-convex path, which is verified by various experiments in this paper. In such a context, our analysis shows that SGD, although has long been considered as a randomized algorithm, converges in an intrinsically deterministic manner to a global minimum.

Isolating effects of age with fair representation learning when assessing dementia    

tl;dr Show that age confounds cognitive impairment detection + solve with fair representation learning + propose metrics and models.

One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts. However, these linguistic features are impacted in a similar but different fashion by the normal aging process. Aging is therefore a confounding factor, whose effects have been hard for machine learning classifiers to isolate. In this paper, we show that deep neural network (DNN) classifiers can infer ages from linguistic features, which is an entanglement that could lead to unfairness across age groups. We show this problem is caused by undesired activations of v-structures in causality diagrams, and it could be addressed with fair representation learning. We build neural network classifiers that learn low-dimensional representations reflecting the impacts of dementia yet discarding the effects of age. To evaluate these classifiers, we specify a model-agnostic score $\Delta_{eo}^{(N)}$ measuring how classifier results are disentangled from age. Our best models outperform baseline neural network classifiers in disentanglement, while compromising accuracy by as little as 2.56\% and 2.25\% on DementiaBank and the Famous People dataset respectively.


tl;dr Bregman's dilemma is shown in deep learning that improvement of margins of over-parameterized models may result in overfitting, and dynamics of normalized margin distributions are proposed to predict generalization error and identify such a dilemma.

A belief persists long in machine learning that enlargement of margins over training data accounts for the resistance of models to overfitting by increasing the robustness. Yet Breiman shows a dilemma (Breiman, 1999) that a uniform improvement on margin distribution \emph{does not} necessarily reduces generalization error. In this paper, we revisit Breiman's dilemma in deep neural networks with recently proposed normalized margins using Lipschitz constant bound by spectral norm products. With both simplified theory and extensive experiments, Breiman's dilemma is shown to rely on dynamics of normalized margin distributions, that reflects the trade-off between model expression power and data complexity. When the complexity of data is comparable to the model expression power in the sense that training and test data share similar phase transitions in normalized margin dynamics, two efficient ways are derived via classic margin-based generalization bounds to successfully predict the trend of generalization error. On the other hand, over-expressed models that exhibit uniform improvements on training normalized margins may lose such a prediction power and fail to prevent the overfitting.

Information-Directed Exploration for Deep Reinforcement Learning    

tl;dr We develop a practical extension of Information-Directed Sampling for Reinforcement Learning, which accounts for parametric uncertainty and heteroscedasticity in the return distribution for exploration.

Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropriately account for heteroscedasticity, even in the bandit setting. Motivated by recent findings that address this issue in bandits, we propose to use Information-Directed Sampling (IDS) for exploration in reinforcement learning. As our main contribution, we build on recent advances in distributional reinforcement learning and propose a novel, tractable approximation of IDS for deep Q-learning. The resulting exploration strategy explicitly accounts for both parametric uncertainty and heteroscedastic observation noise. We evaluate our method on Atari games and demonstrate a significant improvement over alternative approaches.

Attention, Learn to Solve Routing Problems!    

tl;dr Attention based model trained with REINFORCE with greedy rollout baseline to learn heuristics with competitive results on TSP and other routing problems

The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development. However, in order to push this idea towards practical implementation, we need better models and better ways of training. We contribute in both directions: we propose a model based entirely on attention layers with benefits over the Pointer Network and we show how to train this model using REINFORCE with a simple baseline based on a deterministic greedy rollout, which we find is much more efficient than using a value function. We significantly improve over recent learned heuristics for the Travelling Salesman Problem (TSP), getting close to optimal results for problems up to 100 nodes. With the same hyperparameters, we learn strong heuristics for two variants of the Vehicle Routing Problem (VRP), the Orienteering Problem (OP) and (a stochastic variant of) the Prize Collecting TSP (PCTSP), outperforming a wide range of baselines and getting results close to highly optimized and specialized algorithms. The ability to construct a tour in order is beneficial in the online (stochastic) setting. We make code publicly available.

L2-Nonexpansive Neural Networks    

No tl;dr =[

This paper proposes a class of well-conditioned neural networks in which a unit amount of change in the inputs causes at most a unit amount of change in the outputs or any of the internal layers. We develop the known methodology of controlling Lipschitz constants to realize its full potential in maximizing robustness, with a new regularization scheme for linear layers, new ways to adapt nonlinearities and a new loss function. With MNIST and CIFAR-10 classifiers, we demonstrate a number of advantages. Without needing any adversarial training, the proposed classifiers exceed the state of the art in robustness against white-box L2-bounded adversarial attacks. They generalize better than ordinary networks from noisy data with partially random labels. Their outputs are quantitatively meaningful and indicate levels of confidence and generalization, among other desirable properties.

Improving Generalization and Stability of Generative Adversarial Networks    

tl;dr We propose a zero-centered gradient penalty for improving generalization and stability of GANs

Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator. We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator. The penalty guarantees the generalization and convergence of GANs. Experiments on synthetic and large scale datasets verify our theoretical analysis.

Adaptive Input Representations for Neural Language Modeling    

tl;dr Variable capacity input word embeddings and SOTA on WikiText-103, Billion Word benchmarks.

We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. We achieve a new state of the art on the WikiText-103 benchmark of 20.51 perplexity, improving the next best known result by 8.7 perplexity. On the Billion-Word benchmark, we achieve a state of the art of 24.14 perplexity.

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology    

tl;dr We develop a new topological complexity measure for deep neural networks and demonstrate that it captures their salient properties.

While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data. Measures for characterizing and monitoring structural properties, however, have not been developed. In this work, we propose neural persistence, a complexity measure for neural network architectures based on topological data analysis on weighted stratified graphs. To demonstrate the usefulness of our approach, we show that neural persistence agrees with best practices developed in the deep learning community such as dropout and batch normalization. Moreover, we derive a neural persistence-based stopping criterion that shortens the training process while preserving accuracy compared to validation-loss based early stopping.

A variational Dirichlet framework for out-of-distribution detection    

tl;dr A new framework based variational inference for out-of-distribution detection

With the recently rapid development in deep learning, deep neural networks have been widely adopted in many real-life applications. However, deep neural networks are known to have very little control over its uncertainty for test examples, which can potentially cause very harmful and annoying consequences in practical scenarios. In this paper, we are particularly interested in designing a higher-order uncertainty metric for deep neural networks and investigate its performance on the out-of-distribution detection task proposed by~\cite{hendrycks2016baseline}. Our method is based on a variational inference framework where we interpret the output distribution $p(x)$ as a stochastic variable $z$ lying on a simplex of multi-dimensional space and represent the higher-order uncertainty via the entropy of the latent distribution $p(z)$. Under the variational Bayesian framework with a given dataset $D$, we propose to adopt Dirichlet distribution as the approximate posterior $F_{\theta}(z|x)$ to approach the true posterior distribution $p(z|D)$ by maximizing the evidence lower bound of marginal likelihood. By identifying the over-concentration issue in the Dirichlet framework, we further design a log-scaling smoothing function to avert such issue and greatly increase the robustness of the entropy-based uncertainty measure. Through comprehensive experiments on various datasets and architectures, our proposed variational Dirichlet framework is observed to yield state-of-the-art results for out-of-distribution detection.

Successor Options : An Option Discovery Algorithm for Reinforcement Learning    

tl;dr An option discovery method for Reinforcement Learning using the Successor Representation

Hierarchical Reinforcement Learning is a popular method to exploit temporal abstractions in order to tackle the curse of dimensionality. The options framework is one such hierarchical framework that models the notion of skills or options. However, learning a collection of task-agnostic transferable skills is a challenging task. Option discovery typically entails using heuristics, the majority of which revolve around discovering bottleneck states. In this work, we adopt a method complementary to the idea of discovering bottlenecks. Instead, we attempt to discover ``landmark" sub-goals which are prototypical states of well connected regions. These sub-goals are points from which densely connected set of states are easily accessible. We propose a new model called Successor options that leverages Successor Representations to achieve the same. We also design a novel pseudo-reward for learning the intra-option policies. Additionally, we describe an Incremental Successor options model that iteratively builds options and explores in environments where exploration through primitive actions is inadequate to form the Successor Representations. Finally, we demonstrate the efficacy of our approach on a collection of grid worlds and on complex high dimensional environments like Deepmind-Lab.

TTS-GAN: a generative adversarial network for style modeling in a text-to-speech system    

tl;dr a generative adversarial network for style modeling in a text-to-speech system

The modeling of style when synthesizing natural human speech from text has been the focus of significant attention. Some state-of-the-art approaches train an encoder-decoder network on paired text and audio samples (x_txt, x_aud) by encouraging its output to reconstruct x_aud. The synthesized audio waveform is expected to contain the verbal content of x_txt and the auditory style of x_aud. Unfortunately, modeling style in TTS is somewhat under-determined and training models with a reconstruction loss alone is insufficient to disentangle content and style from other factors of variation. In this work, we introduce TTS-GAN, an end-to-end TTS model that offers enhanced content-style disentanglement ability and controllability. We achieve this by combining a pairwise training procedure, an adversarial game, and a collaborative game into one training scheme. The adversarial game concentrates the true data distribution, and the collaborative game minimizes the distance between real samples and generated samples in both the original space and the latent space. As a result, TTS-GAN delivers a highly controllable generator, and a disentangled representation. Benefiting from the separate modeling of style and content, TTS-GAN can generate human fidelity speech that satisfies the desired style conditions. TTS-GAN achieves start-of-the-art results across multiple tasks, including style transfer (content and style swapping), emotion modeling, and identity transfer (fitting a new speaker's voice).

Learning More Interpretable, Backpropagation-Free Deep Architectures with Kernels    

tl;dr We combine kernel method with connectionist models and show that the resulting deep architectures can be trained layer-wise and have more transparent learning dynamics.

One can substitute each neuron in any neural network with a kernel machine and obtain a counterpart powered by kernel machines. The new network inherits the expressive power and architecture of the original but works in a more intuitive way since each node enjoys the simple interpretation as a hyperplane (in a reproducing kernel Hilbert space). Further, using the kernel multilayer perceptron as an example, we prove that in classification, an optimal representation that minimizes the risk of the network can be characterized for each hidden layer. This result removes the need of backpropagation in learning the model and can be generalized to any feedforward kernel network. Moreover, unlike backpropagation, which turns models into black boxes, the optimal hidden representation enjoys an intuitive geometric interpretation, making the dynamics of learning in a deep kernel network simple to understand. Empirical results are provided to validate our theory.

End-to-End Hierarchical Text Classification with Label Assignment Policy    

No tl;dr =[

We present an end-to-end reinforcement learning approach to hierarchical text classification where documents are labeled by placing them at the right positions in a given hierarchy. While existing “global” methods construct hierarchical losses for model training, they either make “local” decisions at each hierarchy node or ignore the hierarchy structure during inference. To close the gap between training/inference and optimize holistic metrics in an end-to-end manner, we propose to learn a label assignment policy to determine where to place the documents and when to stop. The proposed method, HiLAP, optimizes holistic metrics over the hierarchy, makes inter-dependent decisions during inference, and can be combined with different text encoding models for end-to-end training. Experiments on three public datasets show that HiLAP yields an average improvement of 33.4% in Macro-F1 and 5.0% in Samples-F1, outperforming state-of-the-art methods by a large margin.

The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects    

tl;dr We provide theoretical and empirical analysis on the role of anisotropic noise introduced by stochastic gradient on escaping from minima.

Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects. A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency. We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well. We verify our understanding through comparing this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.

Laplacian Networks: Bounding Indicator Function Smoothness for Neural Networks Robustness    

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For the past few years, Deep Neural Network (DNN) robustness has become a question of paramount importance. As a matter of fact, in sensitive settings misclassification can lead to dramatic consequences. Such misclassifications are likely to occur when facing adversarial attacks, hardware failures or limitations, and imperfect signal acquisition. To address this question, authors have proposed different approaches aiming at increasing the robustness of DNNs, such as adding regularizers or training using noisy examples. In this paper we propose a new regularizer built upon the Laplacian of similarity graphs obtained from the representation of training data at each layer of the DNN architecture. This regularizer penalizes large changes (across consecutive layers in the architecture) in the distance between examples of different classes, and as such enforces smooth variations of the class boundaries. Since it is agnostic to the type of deformations that are expected when predicting with the DNN, the proposed regularizer can be combined with existing ad-hoc methods. We provide theoretical justification for this regularizer and demonstrate its effectiveness to improve robustness of DNNs on classical supervised learning vision datasets.

Low-Cost Parameterizations of Deep Convolutional Neural Networks    

tl;dr This paper introduces efficient and economic parametrizations of convolutional neural networks motivated by partial differential equations

Convolutional Neural Networks (CNNs) filter the input data using a series of spatial convolution operators with compactly supported stencils and point-wise nonlinearities. Commonly, the convolution operators couple features from all channels. For wide networks, this leads to immense computational cost in the training of and prediction with CNNs. In this paper, we present novel ways to parameterize the convolution more efficiently, aiming to decrease the number of parameters in CNNs and their computational complexity. We propose new architectures that use a sparser coupling between the channels and thereby reduce both the number of trainable weights and the computational cost of the CNN. Our architectures arise as new types of residual neural network (ResNet) that can be seen as discretizations of a Partial Differential Equations (PDEs) and thus have predictable theoretical properties. Our first architecture involves a convolution operator with a special sparsity structure, and is applicable to a large class of CNNs. Next, we present an architecture that can be seen as a discretization of a diffusion reaction PDE, and use it with three different convolution operators. We outline in our experiments that the proposed architectures, although considerably reducing the number of trainable weights, yield comparable accuracy to existing CNNs that are fully coupled in the channel dimension.

Stochastic Optimization of Sorting Networks via Continuous Relaxations    

tl;dr We provide a continuous relaxation to the sorting operator, enabling end-to-end, gradient-based stochastic optimization.

Sorting input objects is an important step within many machine learning pipelines. However, the sorting operator is non-differentiable w.r.t. its inputs, which prohibits end-to-end gradient-based optimization. In this work, we propose a general-purpose continuous relaxation of the output of the sorting operator from permutation matrices to the set of "unimodal matrices". Further, we use this relaxation to enable more efficient stochastic optimization over the combinatorially large space of permutations. In particular, we derive a reparameterized gradient estimator for the widely used Plackett-Luce family of distributions. We demonstrate the usefulness of our framework on three tasks that require learning semantic orderings of high-dimensional objects.

Magic Tunnels    

tl;dr If optimization gets stuck in a saddle, we add a filter to a CNN in a specific way in order to escape the saddle.

Hierarchically embedding smaller networks in larger networks, e.g.~by increasing the number of hidden units, has been studied since the 1990s. The main interest was in understanding possible redundancies in the parameterization, as well as in studying how such embeddings affect critical points. We take these results as a point of departure to devise a novel strategy for escaping from flat regions of the error surface and to address the slow-down of gradient-based methods experienced in plateaus of saddle points. The idea is to expand the dimensionality of a network in a way that guarantees the existence of new escape directions. We call this operation the opening of a tunnel. One may then continue with the larger network either temporarily, i.e.~closing the tunnel later, or permanently, i.e.~iteratively growing the network, whenever needed. We develop our method for fully-connected as well as convolutional layers. Moreover, we present a practical version of our algorithm that requires no network structure modification and can be deployed as plug-and-play into any current deep learning framework. Experimentally, our method shows significant speed-ups.

Generating Multiple Objects at Spatially Distinct Locations    

tl;dr Extend GAN architecture to obtain control over locations and identities of multiple objects within generated images.

Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as image captions. Furthermore, conditional GANs allow us to control the image generation process through labels or even natural language descriptions. However, fine-grained control of the image layout, i.e. where in the image specific objects should be located, is still difficult to achieve. This is especially true for images that should contain multiple distinct objects at different spatial locations. We introduce a new approach which allows us to control the location of arbitrarily many objects within an image by adding an object pathway to both the generator and the discriminator. Our approach does not need a detailed semantic layout but only bounding boxes and the respective labels of the desired objects are needed. The object pathway focuses solely on the individual objects and is iteratively applied at the locations specified by the bounding boxes. The global pathway focuses on the image background and the general image layout. We perform experiments on the Multi-MNIST, CLEVR, and the more complex MS-COCO data set. Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations. We further show that the object pathway focuses on the individual objects and learns features relevant for these, while the global pathway focuses on global image characteristics and the image background.

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning    

tl;dr We translate a bound on sub-optimality of representations to a practical training objective in the context of hierarchical reinforcement learning.

We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach. Accordingly, the choice of representation -- the mapping of observation space to goal space -- is crucial. To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation. We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice. Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods.

A rotation-equivariant convolutional neural network model of primary visual cortex    

tl;dr A rotation-equivariant CNN model of V1 that outperforms previous models and suggest functional cell types in V1.

Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predictneural responses to natural stimuli accurately. Recent work shows that modelsbased on convolutional neural networks (CNNs) lead to much more accurate pre-dictions, but it remains unclear which features are extracted by V1 neurons beyondorientation selectivity and phase invariance. Here we work towards systematicallystudying V1 computations by categorizing neurons into groups that perform similarcomputations. We present a framework to identify common features independentof individual neurons’ orientation selectivity by using a rotation-equivariant con-volutional neural network, which automatically extracts every feature at multipledifferent orientations. We fit this model to responses of a population of 6000 neu-rons to natural images recorded in mouse primary visual cortex using two-photonimaging. We show that our rotation-equivariant network not only outperforms aregular CNN with the same number of feature maps, but also reveals a numberof common features shared by many V1 neurons, which deviate from the typicaltextbook idea of V1 as a bank of Gabor filters. Our findings are a first step towardsa powerful new tool to study the nonlinear computations in V1.

Cumulative Saliency based Globally Balanced Filter Pruning For Efficient Convolutional Neural Networks    

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This paper propose a Cumulative Saliency based Globally Balanced Filter Pruning (GBFP) scheme to prune redundant filters of Convolutional Neural Networks (CNNs). Specifically, the GBFP adopts a balanced pruning method, which not only measures the global redundancy of the filter in the whole model but also considers the importance of the current layer. Secondly, in the model pruning recovery process, we use the cumulative saliency strategy to improve the accuracy of pruning. GBFP has two advantages over previous works: (1) More accurate pruning guidance. For a pre-trained CNN model, the saliency of the filter varies with different input data. Therefore, accumulating the saliency of the filter over the entire data set can provide more accurate guidance for pruning. (2) More balanced pruning results. Before globally pruning the unsalient filters across all layers, the proposed first normalizes the saliency of each layer, which prevents unbalanced pruning results due to uneven distribution of parameters in each layer.


tl;dr This paper propose a mask method which solves the previous blurred results of unsupervised monocular depth estimation caused by occlusion

Unsupervised monocular depth estimation has made great progress after deep learning is involved. Training with binocular stereo images is considered as a good option as the data can be easily obtained. However, the depth or disparity prediction results show poor performance for the object boundaries. The main reason is related to the handling of occlusion areas during the training. In this paper, we propose a novel method to overcome this issue. Exploiting disparity maps property, we generate an occlusion mask to block the back-propagation of the occlusion areas during image warping. We also design new networks with flipped stereo images to induce the networks to learn occluded boundaries. It shows that our method achieves clearer boundaries and better evaluation results on KITTI driving dataset and Virtual KITTI dataset.

Fast adversarial training for semi-supervised learning    

tl;dr We propose a fast and efficient semi-supervised learning method using adversarial training.

In semi-supervised learning, Bad GAN approach is one of the most attractive method due to the intuitional simplicity and powerful performances. Bad GAN learns a classifier with bad samples distributed on complement of the support of the input data. But Bad GAN needs additional architectures, a generator and a density estimation model, which involves huge computation and memory consumption cost. VAT is another good semi-supervised learning algorithm, which utilizes unlabeled data to improve the invariance of the classifier with respect to perturbation of inputs. In this study, we propose a new method by combining the ideas of Bad GAN and VAT. The proposed method generates bad samples of high-quality by use of the adversarial training used in VAT. We give theoretical explanations why the adversarial training is good at both generating bad samples and semi-supervised learning. An advantage of the proposed method is to achieve the competitive performances with much fewer computations. We demonstrate this advantage by analyzing three well known benchmark image datasets.

Supervised Community Detection with Line Graph Neural Networks    

tl;dr We propose a novel graph neural network architecture based on the non-backtracking operator defined over edge adjacencies and demonstrate its effectiveness on community detection tasks on graphs.

We study data-driven methods for community detection on graphs, an inverse problem that is typically solved in terms of the spectrum of certain operators or via posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational signal-to-noise detection thresholds. This graph inference task can be recast as a node-wise graph classification problem, and, as such, computational detection thresholds can be studied in terms of learning within appropriate models. We present a novel family of Graph Neural Networks (GNNs) and show that they can reach those detection thresholds in a purely data-driven manner without access to the underlying generative models, and even improve upon current computational thresholds in hard regimes. For that purpose, we propose to augment GNNs with the non-backtracking operator, defined on the line graph of edge adjacencies. We also perform the first analysis of optimization landscape on using GNNs to solve community detection problems, demonstrating that under certain simplifications and assumptions, the loss value at the local minima is close to the loss value at the global minimum/minima. Finally, the resulting model is also tested on real datasets, performing significantly better than previous models.

Evaluation Methodology for Attacks Against Confidence Thresholding Models    

tl;dr We present metrics and an optimal attack for evaluating models that defend against adversarial examples using confidence thresholding

Current machine learning algorithms can be easily fooled by adversarial examples. One possible solution path is to make models that use confidence thresholding to avoid making mistakes. Such models refuse to make a prediction when they are not confident of their answer. We propose to evaluate such models in terms of tradeoff curves with the goal of high success rate on clean examples and low failure rate on adversarial examples. Existing untargeted attacks developed for models that do not use confidence thresholding tend to underestimate such models' vulnerability. We propose the MaxConfidence family of attacks, which are optimal in a variety of theoretical settings, including one realistic setting: attacks against linear models. Experiments show the attack attains good results in practice. We show that simple defenses are able to perform well on MNIST but not on CIFAR, contributing further to previous calls that MNIST should be retired as a benchmarking dataset for adversarial robustness research. We release code for these evaluations as part of the cleverhans (Papernot et al 2018) library (ICLR reviewers should be careful not to look at who contributed these features to cleverhans to avoid de-anonymizing this submission).

Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank    

tl;dr Personalized embedding propagation combines neural networks with personalized PageRank for semi-supervised classification on graphs.

Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood cannot be easily extended. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct personalized embedding propagation (PEP) and its approximation, PEP$_\text{A}$. Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification on multiple graphs in the most thorough study done so far for GCN-like models.

Slimmable Neural Networks    

tl;dr We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime.

We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individual networks with different width multipliers, we train a shared network with switchable batch normalization. At runtime, the network can adjust its width on the fly according to on-device benchmarks and resource constraints, rather than downloading and offloading different models. Our trained networks, named slimmable neural networks, achieve similar (and in many cases better) ImageNet classification accuracy than individually trained models of MobileNet v1, MobileNet v2, ShuffleNet and ResNet-50 at different widths respectively. We also demonstrate better performance of slimmable models compared with individual ones across a wide range of applications including COCO bounding-box object detection, instance segmentation and person keypoint detection without tuning hyper-parameters. Lastly we visualize and discuss the learned features of slimmable networks. Code and models will be released.

RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks    

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Explicit encoding of group actions in deep features makes it possible for convolutional neural networks (CNNs) to handle global deformations of images, which is critical to success in many vision tasks. This paper proposes to decompose the convolutional filters over joint steerable bases across the space and the group geometry simultaneously, namely a rotation-equivariant CNN with decomposed convolutional filters (RotDCF). This decomposition facilitates computing the joint convolution, which is proved to be necessary for the group equivariance. It significantly reduces the model size and computational complexity while preserving performance, and truncation of the bases expansion serves implicitly to regularize the filters. On datasets involving in-plane and out-of-plane object rotations, RotDCF deep features demonstrate greater robustness and interpretability than regular CNNs. The stability of the equivariant representation to input variations is also proved theoretically. The RotDCF framework can be extended to groups other than rotations, providing a general approach which achieves both group equivariance and representation stability at a reduced model size.

Dynamic Sparse Graph for Efficient Deep Learning    

tl;dr We construct dynamic sparse graph via dimension-reduction search to reduce compute and memory cost in both DNN training and inference.

We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference. The great success of DNNs motivates the pursuing of lightweight models for the deployment onto embedded devices. However, most of the previous studies optimize for inference while neglect training or even complicate it. Training is far more intractable, since (i) the neurons dominate the memory cost rather than the weights in inference; (ii) the dynamic activation makes previous sparse acceleration via one-off optimization on fixed weight invalid; (iii) batch normalization (BN) is critical for maintaining accuracy while its activation reorganization damages the sparsity. To address these issues, DSG activates only a small amount of neurons with high selectivity at each iteration via a dimension-reduction search (DRS) and obtains the BN compatibility via a double-mask selection (DMS). Experiments show significant memory saving (1.7-4.5x) and operation reduction (2.3-4.4x) with little accuracy loss on various benchmarks.

The Unreasonable Effectiveness of (Zero) Initialization in Deep Residual Learning    

tl;dr All you need to train deep residual networks is a good initialization; normalization layers are not necessary.

Normalization layers are a staple in state-of-the-art deep neural network architectures. They are widely believed to stabilize training, enable higher learning rate, accelerate convergence and improve generalization, though the reason for their effectiveness is still an active research topic. In this work, we challenge the commonly-held beliefs by showing that none of the perceived benefits is unique to normalization. Specifically, we propose ZeroInit, an initialization motivated by solving the exploding and vanishing gradient problem at the beginning of training by initializing as a zero function. We find training residual networks with ZeroInit to be as stable as training with normalization - even for networks with 10,000 layers. Furthermore, with proper regularization, ZeroInit without normalization matches or exceeds the performance of state-of-the-art residual networks in image classification and machine translation.

The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions    

tl;dr Examined how a simple ensemble approach can tackle the biggest challenges in Q-learning.

Reinforcement learning agents learn by exploring the environment and then exploiting what they have learned. This frees the human trainers from having to know the preferred action or intrinsic value of each encountered state. The cost of this freedom is reinforcement learning is slower and more unstable than supervised learning. We explore the possibility that ensemble methods can remedy these shortcomings and do so by investigating a novel technique which harnesses the wisdom of the crowds by bagging Q-function approximator estimates. Our results show that this proposed approach improves all three tasks and reinforcement learning approaches attempted. We are able to demonstrate that this is a direct result of the increased stability of the action portion of the state-action-value function used by Q-learning to select actions and by policy gradient methods to train the policy.

Bayesian Modelling and Monte Carlo Inference for GAN    

tl;dr A novel Bayesian treatment for GAN with theoretical guarantee.

Bayesian modelling is a principal framework to perform model aggregation, which has been a primary mechanism to combat mode collapsing in the context of Generative Adversarial Networks (GANs). In this paper, we propose a novel Bayesian modelling framework for GANs, which iteratively learns a distribution over generators with a carefully crafted prior. Learning is efficiently triggered by a tailored stochastic gradient Hamiltonian Monte Carlo with novel gradient approximation to perform Bayesian inference. Our theoretical analysis further reveals that our treatment is the first Bayesian modelling framework that yields an equilibrium where generator distributions are faithful to the data distribution. Empirical evidence on synthetic high-dimensional multi-modal data and the natural image database CIFAR-10 demonstrates the superiority of our method over both start-of-the-art multi-generator GANs and other Bayesian treatment for GANs.


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Adversarial examples, referred to as augmented data points generated by imperceptible perturbation of input samples, have recently drawn much attention. Well-crafted adversarial examples may even mislead state-of-the-art deep models to make wrong predictions easily. To alleviate this problem, many studies focus on investigating how adversarial examples can be generated and/or resisted. All the existing work handles this problem in the Euclidean space, which may however be unable to describe data geometry. In this paper, we propose a generalized framework that addresses the learning problem of adversarial examples with Riemannian geometry. Specifically, we define the local coordinate systems on Riemannian manifold, develop a novel model called Adversarial Training with Riemannian Manifold, and design a series of theory that manages to learn the adversarial examples in the Riemannian space feasibly and efficiently. The proposed work is important in that (1) it is a generalized learning methodology since Riemmanian manifold space would be degraded to the Euclidean space in a special case; (2) it is the first work to tackle the adversarial example problem tractably through the perspective of geometry; (3) from the perspective of geometry, our method leads to the steepest direction of the loss function. We also provide a series of theory showing that our proposed method can truly find the decent direction for the loss function with a comparable computational time against traditional adversarial methods. Finally, the proposed framework demonstrates superior performance to the traditional counterpart methods on benchmark data including MNIST, CIFAR-10 and SVHN.

Exploration by random distillation    

tl;dr A simple exploration bonus is introduced and achieves state of the art performance in 3 hard exploration Atari games.

We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method to flexibly combine intrinsic and extrinsic rewards. We find that the random network distillation (RND) bonus combined with this increased flexibility enables significant progress on several hard exploration Atari games. In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods. To the best of our knowledge, this is the first method that achieves better than average human performance on this game without using demonstrations or having access the underlying state of the game, and occasionally completes the first level. This suggests that relatively simple methods that scale well can be sufficient to tackle challenging exploration problems.

Computing committor functions for the study of rare events using deep learning with importance sampling    

tl;dr Computing committor functions for rare events

The committor function is a central object of study in understanding transitions between metastable states in complex systems. However, computing the committor function for realistic systems at low temperatures is a challenging task, due to the curse of dimensionality and the scarcity of transition data. In this paper, we introduce a computational approach that overcomes these issues and achieves good performance on complex benchmark problems with rough energy landscapes. The new approach combines deep learning, importance sampling and feature engineering techniques. This establishes an alternative practical method for studying rare transition events among metastable states of complex, high dimensional systems.

Synthnet: Learning synthesizers end-to-end    

tl;dr A convolutional autoregressive generative model that generates high fidelity audio, behchmarked on music

Learning synthesizers and generating music in the raw audio domain is a challenging task. We investigate the learned representations of convolutional autoregressive generative models. Consequently, we show that mappings between musical notes and the harmonic style (instrument timbre) can be learned based on the raw audio music recording and the musical score (in binary piano roll format). Our proposed architecture, SynthNet uses minimal training data (9 minutes), is substantially better in quality and converges 6 times faster than the baselines. The quality of the generated waveforms (generation accuracy) is sufficiently high that they are almost identical to the ground truth. Therefore, we are able to directly measure generation error during training, based on the RMSE of the Constant-Q transform. Mean opinion scores are also provided. We validate our work using 7 distinct harmonic styles and also provide visualizations and links to all generated audio.

Unsupervised Learning of the Set of Local Maxima    

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This paper describes a new form of unsupervised learning, whose input is a set of unlabeled points that are assumed to be local maxima of an unknown value function in an unknown subset of the vector space. Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h that given two nearby samples, predicts which sample has the higher value. Loss terms are used to ensure that all training samples x are a local maxima, according to h and satisfy c(x)=1. Therefore, c and h provide training signals to each other: a point x' in the vicinity of x satisfies c(x)=-1 or is deemed by h to be lower in value than x. We present an algorithm, show an example where it is more efficient to use local maxima as an indicator function than to employ conventional classification, and derive a suitable generalization bound. Our experiments show that the method is able to outperform one-class classification algorithms in the task of anomaly detection and also provide an additional signal that is extracted in a completely unsupervised way.

On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization    

tl;dr We analyze convergence of Adam-type algorithms and provide mild sufficient conditions to guarantee their convergence, we also show violating the conditions can makes an algorithm diverge.

This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the ''``Adam-type'', includes the popular algorithms such as Adam, AMSGrad, AdaGrad. Despite their popularity in training deep neural networks (DNNs), the convergence of these algorithms for solving non-convex problems remains an open question. In this paper, we develop an analysis framework and a set of mild sufficient conditions that guarantee the convergence of the Adam-type methods, with a convergence rate of order $O(\log{T}/\sqrt{T})$ for non-convex stochastic optimization. Our convergence analysis applies to a new algorithm called AdaFom (AdaGrad with First Order Momentum). We show that the conditions are essential, by identifying concrete examples in which violating the conditions makes an algorithm diverge. Besides providing one of the first comprehensive analysis for Adam-type methods in the non-convex setting, our results can also help the practitioners to easily monitor the progress of algorithms and determine their convergence behavior.

Discriminative out-of-distribution detection for semantic segmentation    

tl;dr We present a novel approach for detecting out-of-distribution pixels in semantic segmentation.

Most classification and segmentation datasets assume a closed-world scenario in which predictions are expressed as distribution over a predetermined set of visual classes. However, such assumption implies unavoidable and often unnoticeable failures in presence of out-of-distribution (OOD) input. These failures are bound to happen in most real-life applications since current visual ontologies are far from being comprehensive. We propose to address this issue by discriminative detection of OOD pixels in input data. Different from recent approaches, we avoid to bring any decisions by only observing the training dataset of the primary model trained to solve the desired computer vision task. Instead, we train a dedicated OOD model which discriminates the primary training set from a much larger "background" dataset which approximates the variety of the visual world. We perform our experiments on high resolution natural images in a dense prediction setup. We use several road driving datasets as our training distribution, while we approximate the background distribution with the ILSVRC dataset. We evaluate our approach on WildDash test, which is currently the only public test dataset with out-of-distribution images. The obtained results show that the proposed approach succeeds to identify out-of-distribution pixels while outperforming previous work by a wide margin.

Generative Adversarial Models for Learning Private and Fair Representations    

tl;dr We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations with certified privacy/fairness guarantees

We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations. GAPF leverages recent advancements in adversarial learning to allow a data holder to learn "universal" representations that decouple a set of sensitive attributes from the rest of the dataset. Under GAPF, finding the optimal privacy mechanism is formulated as a constrained minimax game between a private/fair encoder and an adversary. We show that for appropriately chosen adversarial loss functions, GAPF provides privacy guarantees against strong information-theoretic adversaries and enforces demographic parity. We also evaluate the performance of GAPF on multi-dimensional Gaussian mixture models and real datasets, and show how a designer can certify that representations learned under an adversary with a fixed architecture perform well against more complex adversaries.

Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models    

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Sequence to sequence (seq2seq) models have become a popular framework for neural sequence prediction. While traditional seq2seq models are trained by Maximum Likelihood Estimation (MLE), much recent work has made various attempts to optimize evaluation scores directly to solve the mismatch between training and evaluation, since model predictions are usually evaluated by a task specific evaluation metric like BLEU or ROUGE scores instead of perplexity. This paper for the first time puts this existing work into two categories, a) minimum divergence, and b) maximum margin. We introduce a new training criterion based on the analysis of existing work, and empirically compare models in the two categories. Our experimental results show that training criteria based on the idea of minimum divergence can usually work better than maximum margin methods, on both the tasks of machine translation and sentence summarization.

The Problem of Model Completion    

tl;dr We study empirically how hard it is to recover missing parts of trained models

We introduce the problem of model completion: Given the entire training data set oran environment simulator, and a subset of the parameters of a trained deep learning model, how much training is required to recover the model's original performance? We define a metric for evaluating the hardness of the model completion problem and study it empirically in both supervised learning on ImageNet and reinforcement learning on Atari and DeepMind Lab. Our experiments show that (1) the model completion problem is harder in reinforcement learning than in supervised learning because of the unavailability of the trained agent’s trajectories, and (2) its hardness depends not primarily on the number of parameters of the missing part, but more so on their type and location.

Convolutional CRFs for Semantic Segmentation    

tl;dr We propose Convolutional CRFs a fast, powerful and trainable alternative to Fully Connected CRFs.

For the challenging semantic image segmentation task the best performing models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In more recent works however, CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow training and inference speeds of CRFs, as well as the difficulty of learning the internal CRF parameters. To overcome both issues we propose to add the assumption of conditional independence to the framework of fully-connected CRFs. This allows us to reformulate the inference in terms of convolutions, which can be implemented highly efficiently on GPUs.Doing so speeds up inference and training by two orders of magnitude. All parameters of the convolutional CRFs can easily be optimized using backpropagation. Towards the goal of facilitating further CRF research we have made our implementations publicly available.

GradMix: Multi-source Transfer across Domains and Tasks    

tl;dr We propose a gradient-based method to transfer knowledge from multiple sources across different domains and tasks.

The machine learning and computer vision community is witnessing an unprecedented rate of new tasks being proposed and addressed, thanks to the power of deep convolutional networks to find complex mappings from X to Y. The advent of each task often accompanies the release of a large-scale human-labeled dataset, for supervised training of the deep network. However, it is expensive and time-consuming to manually label sufficient amount of training data. Therefore, it is important to develop algorithms that can leverage off-the-shelf labeled dataset to learn useful knowledge for the target task. While previous works mostly focus on transfer learning from a single source, we study multi-source transfer across domains and tasks (MS-DTT), in a semi-supervised setting. We propose GradMix, a model-agnostic method applicable to any model trained with gradient-based learning rule. GradMix transfers knowledge via gradient descent, by weighting and mixing the gradients from all sources during training. Our method follows a meta-learning objective, by assigning layer-wise weights to the source gradients, such that the combined gradient follows the direction that can minimize the loss for a small set of samples from the target dataset. In addition, we propose to adaptively adjust the learning rate for each mini-batch based on its importance to the target task, and a pseudo-labeling method to leverage the unlabeled samples in the target domain. We perform experiments on two MS-DTT tasks: digit recognition and action recognition, and demonstrate the advantageous performance of the proposed method against multiple baselines.

Excitation Dropout: Encouraging Plasticity in Deep Neural Networks    

tl;dr We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction.

We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction: the firing of neurons in specific paths. In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout. In essence, we dropout with higher probability those neurons which contribute more to decision making at training time. This approach penalizes high saliency neurons that are most relevant for model prediction, i.e. those having stronger evidence. By dropping such high-saliency neurons, the network is forced to learn alternative paths in order to maintain loss minimization, resulting in a plasticity-like behavior, a characteristic of human brains too. We demonstrate better generalization ability, an increased utilization of network neurons, and a higher resilience to network compression using several metrics over four image/video recognition benchmarks.

GANSynth: Adversarial Neural Audio Synthesis    

tl;dr High-quality audio synthesis with GANs

Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregressive models, such as WaveNet, model local structure at the expense of global latent structure and slow iterative sampling, while Generative Adversarial Networks (GANs), have global latent conditioning and efficient parallel sampling, but struggle to generate locally-coherent audio waveforms. Herein, we demonstrate that GANs can in fact generate high-fidelity and locally-coherent audio by modelling log magnitudes and instantaneous frequencies with sufficient frequency resolution in the spectral domain. Through extensive empirical investigations on the NSynth dataset, we demonstrate that GANs are able to outperform strong WaveNet baselines on automated and human evaluation metrics, and efficiently generate audio ~54,000 times faster than their autoregressive counterparts.

Smoothing the Geometry of Probabilistic Box Embeddings    

tl;dr Improve hierarchical embedding models using kernel smoothing

There is growing interest in geometrically-inspired embeddings for learning hierarchies, partial orders, and lattice structures, with natural applications to transitive relational data such as entailment graphs. Recent work has extended these ideas beyond deterministic hierarchies to probabilistically calibrated models, which enable learning from uncertain supervision and inferring soft-inclusions among concepts, while maintaining the geometric inductive bias of hierarchical embedding models. We build on the Box Lattice model of Vilnis et al. (2018), which showed promising results in modeling soft-inclusions through an overlapping hierarchy of sets, parameterized as high-dimensional hyperrectangles (boxes). However, the hard edges of the boxes present difficulties for standard gradient based optimization; that work employed a special surrogate function for the disjoint case, but we find this method to be fragile. In this work, we present a novel hierarchical embedding model, inspired by a relaxation of box embeddings into parameterized density functions using Gaussian convolutions over the boxes. Our approach provides an alternative surrogate to the original lattice measure that improves the robustness of optimization in the disjoint case, while also preserving the desirable properties with respect to the original lattice. We demonstrate increased or matching performance on WordNet hypernymy prediction, Flickr caption entailment, and a MovieLens-based market basket dataset. We show especially marked improvements in the case of sparse data, where many conditional probabilities should be low, and thus boxes should be nearly disjoint.

Trace-back along capsules and its application on semantic segmentation    

tl;dr A capsule-based semantic segmentation, which the probabilities of the class labels are traced back through capsule layers.

In this paper, we propose a capsule-based neural network model to solve the semantic segmentation problem. By taking advantage of the extractable part-whole dependencies available in capsule layers, we derive the probabilities of the class labels for individual capsules through a layer-by-layer recursive procedure. We model this procedure as a traceback layer, and take it as a central piece to build an end-to-end segmentation network. In addition to object boundaries, image-level class labels are also explicitly sought in our model, which poses a significant advantage over the state-of-the-art fully convolutional network (FCN) solutions. Experiments conducted on modified MNIST and neuroimages demonstrate that our model considerably enhance the segmentation performance compared to the leading FCN variant.

Do Deep Generative Models Know What They Don't Know?    

No tl;dr =[

A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. Generative models are generally viewed to be robust to such overconfidence mistakes as modeling the density of the input features can be used to detect novel, out-of-distribution inputs. In this paper we challenge this assumption, focusing our analysis on flow-based generative models in particular since they are trained and evaluated via the exact marginal likelihood. We find that the model density cannot distinguish images of common objects such as dogs, trucks, and horses (i.e. CIFAR-10) from those of house numbers (i.e. SVHN), assigning a higher likelihood to the latter when the model is trained on the former. We find such behavior persists even when we restrict the flow models to constant-volume transformations. These admit some theoretical analysis, and we show that the difference in likelihoods can be explained by the location and variances of the data and the model curvature, which shows that such behavior is more general and not just restricted to the pairs of datasets used in our experiments. Our results suggest caution when using density estimates of deep generative models on out-of-distribution inputs.

Gradient-based learning for F-measure and other performance metrics    

No tl;dr =[

Many important classification performance metrics, e.g. $F$-measure, are non-differentiable and non-decomposable, and are thus unfriendly to gradient descent algorithm. Consequently, despite their popularity as evaluation metrics, these metrics are rarely optimized as training objectives in neural network community. In this paper, we propose an empirical utility maximization scheme with provable learning guarantees to address the non-differentiability of these metrics. We then derive a strongly consistent gradient estimator to handle non-decomposability. These innovations enable end-to-end optimization of these metrics with the same computational complexity as optimizing a decomposable and differentiable metric, e.g. cross-entropy loss.

Don't let your Discriminator be fooled    

tl;dr A discriminator that is not easily fooled by adversarial example makes GAN training more robust and leads to a smoother objective.

Generative Adversarial Networks are one of the leading tools in generative modeling, image editing and content creation. However, they are hard to train as they require a delicate balancing act between two deep networks fighting a never ending duel. Some of the most promising adversarial models today minimize a Wasserstein objective. It is smoother and more stable to optimize. In this paper, we show that the Wasserstein distance is just one out of a large family of objective functions that yield these properties. By making the discriminator of a GAN robust to adversarial attacks we can turn any GAN objective into a smooth and stable loss. We experimentally show that any GAN objective, including Wasserstein GANs, benefit from adversarial robustness both quantitatively and qualitatively. The training additionally becomes more robust to suboptimal choices of hyperparameters, model architectures, or objective functions.

Is Wasserstein all you need?    

No tl;dr =[

We propose a unified framework for building unsupervised representations of entities and their compositions, by viewing each entity as a histogram over its contexts. This enables us to take advantage of optimal transport and construct representations that effectively harness the geometry of the underlying space containing the contexts. Our method captures uncertainty via modelling the entities as distributions and simultaneously provides interpretability with the optimal transport map, hence giving a novel perspective for building rich and powerful feature representations. As a guiding example, we formulate unsupervised representations for text, and demonstrate it on tasks such as sentence similarity and word entailment detection. Empirical results show strong advantages gained through the proposed framework. This approach can be used for any unsupervised or supervised problem (on text or other modalities) with a co-occurrence structure, such as any sequence data. The key tools at the core of this framework are Wasserstein distances and Wasserstein barycenters, hence raising the question from our title.

On the Margin Theory of Feedforward Neural Networks    

tl;dr We show that training feedforward relu networks with a weak regularizer results in a maximum margin and analyze the implications of this result.

Past works have shown that, somewhat surprisingly, over-parametrization can help generalization in neural networks. Towards explaining this phenomenon, we adopt a margin-based perspective. We establish: 1) for multi-layer feedforward relu networks, the global minimizer of a weakly-regularized cross-entropy loss has the maximum normalized margin among all networks, 2) as a result, increasing the over-parametrization improves the normalized margin and generalization error bounds for two-layer networks. In particular, an infinite-size neural network enjoys the best generalization guarantees. The typical infinite feature methods are kernel methods; we compare the neural net margin with that of kernel methods and construct natural instances where kernel methods have much weaker generalization guarantees. We validate this gap between the two approaches empirically. Finally, this infinite-neuron viewpoint is also fruitful for analyzing optimization. We show that a perturbed gradient flow on infinite-size networks finds a global optimizer in polynomial time.

How to train your MAML    

tl;dr MAML is great, but it has many problems, we solve many of those problems and as a result we learn most hyper parameters end to end, speed-up training and inference and set a new SOTA in few-shot learning

The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem.Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning. MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times. In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML++.

Learning a SAT Solver from Single-Bit Supervision    

tl;dr We train a graph network to predict boolean satisfiability and show that it learns to search for solutions, and that the solutions it finds can be decoded from its activations.

We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not competitive with state-of-the-art SAT solvers, NeuroSAT can solve problems that are substantially larger and more difficult than it ever saw during training by simply running for more iterations. Moreover, NeuroSAT generalizes to novel distributions; after training only on random SAT problems, at test time it can solve SAT problems encoding graph coloring, clique detection, dominating set, and vertex cover problems, all on a range of distributions over small random graphs.

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL    

tl;dr We present MetaMimic, an algorithm that takes as input a demonstration dataset and outputs (i) a one-shot high-fidelity imitation policy (ii) an unconditional task policy.

Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators. MetaMimic relies on the principle of storing all experiences in a memory and replaying these to learn massive deep neural network policies by off-policy RL. This paper introduces, to the best of our knowledge, the largest existing neural networks for deep RL and shows that larger networks with normalization are needed to achieve one-shot high-fidelity imitation on a challenging manipulation task. The results also show that both types of policy can be learned from vision, in spite of the task rewards being sparse, and without access to demonstrator actions.

MILE: A Multi-Level Framework for Scalable Graph Embedding    

tl;dr A generic framework to scale existing graph embedding techniques to large graphs.

Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a novel graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while also often generating embeddings of better quality for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation.

Image Score: how to select useful samples    

No tl;dr =[

There has long been debates on how we could interpret neural networks and understand the decisions our models make. Specifically, why deep neural networks tend to be error-prone when dealing with samples that output low softmax scores. We present an efficient approach to measure the confidence of decision-making steps by statistically investigating each unit's contribution to that decision. Instead of focusing on how the models react on datasets, we study the datasets themselves given a pre-trained model. Our approach is capable of assigning a score to each sample within a dataset that measures the frequency of occurrence of that sample's chain of activation. We demonstrate with experiments that our method could select useful samples to improve deep neural networks in a semi-supervised leaning setting.

Graph U-Net    

tl;dr We propose the graph U-Net based on our novel graph pooling and unpooling layer for network embedding.

We consider the problem of representation learning for graph data. Convolutional neural networks can naturally operate on images, but have significant challenges in dealing with graph data. Given images are special cases of graphs with nodes lie on 2D lattices, graph embedding tasks have a natural correspondence with image pixel-wise prediction tasks such as segmentation. While encoder-decoder architectures like U-Net have been successfully applied on many image pixel-wise prediction tasks, similar methods are lacking for graph data. This is due to the fact that pooling and up-sampling operations are not natural on graph data. To address these challenges, we propose novel graph pooling (gPool) and unpooling (gUnpool) operations in this work. The gPool layer adaptively selects some nodes to form a smaller graph based on their scalar projection values on a trainable projection vector. We further propose the gUnpool layer as the inverse operation of the gPool layer. The gUnpool layer restores the graph into its original structure using the position information of nodes selected in the corresponding gPool layer. Based on our proposed gPool and gUnpool layers, we develop an encoder-decoder model on graph, known as the graph U-Net. Our experimental results on node classification tasks demonstrate that our methods achieve consistently better performance than previous models.

TherML: The Thermodynamics of Machine Learning    

tl;dr We offer a framework for representation learning that connects with a wide class of existing objectives and is analogous to thermodynamics.

In this work we offer an information-theoretic framework for representation learning that connects with a wide class of existing objectives in machine learning. We develop a formal correspondence between this work and thermodynamics and discuss its implications.

Visual Semantic Navigation using Scene Priors    

No tl;dr =[

How do humans navigate to target objects in novel scenes? Do we use the semantic/functional priors we have built over years to efficiently search and navigate? For example, to search for mugs, we search cabinets near the coffee machine and for fruits we try the fridge. In this work, we focus on incorporating semantic priors in the task of semantic navigation. We propose to use Graph Convolutional Networks for incorporating the prior knowledge into a deep reinforcement learning framework. The agent uses the features from the knowledge graph to predict the actions. For evaluation, we use the AI2-THOR framework. Our experiments show how semantic knowledge improves the performance significantly. More importantly, we show improvement in generalization to unseen scenes and/or objects.

GenEval: A Benchmark Suite for Evaluating Generative Models    

tl;dr We introduce battery of synthetic distributions and metrics for measuring the success of generative models

Generative models are important for several practical applications, from low level image processing tasks, to model-based planning in robotics. More generally, the study of generative models is motivated by the long-standing endeavor to model uncertainty and to discover structure by leveraging unlabeled data. Unfortunately, the lack of an ultimate task of interest has hindered progress in the field, as there is no established way to compare models and, often times, evaluation is based on mere visual inspection of samples drawn from such models. In this work, we aim at addressing this problem by introducing a new benchmark evaluation suite, dubbed \textit{GenEval}. GenEval hosts a large array of distributions capturing many important properties of real datasets, yet in a controlled setting, such as lower intrinsic dimensionality, multi-modality, compositionality, independence and causal structure. Any model can be easily plugged for evaluation, provided it can generate samples. Our extensive evaluation suggests that different models have different strenghts, and that GenEval is a great tool to gain insights about how models and metrics work. We offer GenEval to the community~\footnote{Available at: \it{coming soon}.} and believe that this benchmark will facilitate comparison and development of new generative models.

Empirical Study of Easy and Hard Examples in CNN Training    

tl;dr Unknown properties of easy and hard examples are shown, and they come from biases in a dataset and SGD.

Deep Neural Networks (DNNs) generalize well despite their massive size and capability of memorizing all examples. There is a hypothesis that DNNs start learning from simple patterns based on the observations that are consistently well-classified at early epochs (i.e., easy examples) and examples misclassified (i.e., hard examples). However, despite the importance of understanding the learning dynamics of DNNs, properties of easy and hard examples are not fully investigated. In this paper, we study the similarities of easy and hard examples respectively among different CNNs, assessing those examples’ contributions to generalization. Our results show that most easy examples are identical among different CNNs, as they share similar dataset-dependent patterns (e.g., colors, structures, and superficial cues in high-frequency). Moreover, while hard examples tend to contribute more to generalization than easy examples, removing a large number of easy examples leads to poor generalization, and we find that most misclassified examples in validation dataset are hard examples. By analyzing intriguing properties of easy and hard examples, we discover that the reason why easy and hard examples have such properties can be explained by biases in a dataset and Stochastic Gradient Descent (SGD).

Optimization on Multiple Manifolds    

tl;dr This paper introduces an algorithm to handle optimization problem with multiple constraints under vision of manifold.

Optimization on manifold has been widely used in machine learning, to handle optimization problems with constraint. Most previous works focus on the case with a single manifold. However, in practice it is quite common that the optimization problem involves more than one constraints, (each constraint corresponding to one manifold). It is not clear in general how to optimize on multiple manifolds effectively and provably especially when the intersection of multiple manifolds is not a manifold or cannot be easily calculated. We propose a unified algorithm framework to handle the optimization on multiple manifolds. Specifically, we integrate information from multiple manifolds and move along an ensemble direction by viewing the information from each manifold as a drift and adding them together. We prove the convergence properties of the proposed algorithms. We also apply the algorithms into training neural network with batch normalization layers and achieve preferable empirical results.

State-Regularized Recurrent Networks    

tl;dr We introduce stochastic state transition mechanism to RNNs, simplifies finite state automata (FDA) extraction, forces RNNs to operate more like automata with external memory, better extrapolation behavior and interpretability.

Recurrent networks are a widely used class of neural architectures. They have, however, two shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We show that state-regularization (a) simplifies the extraction of finite state automata modeling an RNN's state transition dynamics, and (b) forces RNNs to operate more like automata with external memory and less like finite state machines.

On the Learning Dynamics of Deep Neural Networks    

tl;dr This paper analyzes the learning dynamics of neural networks on classification tasks solved by gradient descent using the cross-entropy and hinge losses.

While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely misunderstood. In this work, we study the case of binary classification and prove various properties of learning in such networks under strong assumptions such as linear separability of the data. Extending existing results from the linear case, we confirm empirical observations by proving that the classification error also follows a sigmoidal shape in nonlinear architectures. We show that given proper initialization, learning expounds parallel independent modes and that certain regions of parameter space might lead to failed training. We also demonstrate that input norm and features' frequency in the dataset lead to distinct convergence speeds which might shed some light on the generalization capabilities of deep neural networks. We provide a comparison between the dynamics of learning with cross-entropy and hinge losses, which could prove useful to understand recent progress in the training of generative adversarial networks. Finally, we identify a phenomenon that we baptize gradient starvation where the most frequent features in a dataset prevent the learning of other less frequent but equally informative features.

Spread Divergences    

tl;dr Using noise to define the divergence between distributions with different support.

For distributions $p$ and $q$ with different support, the divergence $\div{p}{q}$ generally will not exist. We define a spread divergence $\sdiv{p}{q}$ on modified $p$ and $q$ and describe sufficient conditions for the existence of such a divergence. We give examples of using a spread divergence to train implicit generative models, including linear models (Principal Components Analysis and Independent Components Analysis) and non-linear models (Deep Generative Networks).

Clean-Label Backdoor Attacks    

tl;dr We show how to successfully perform backdoor attacks without changing training labels.

Deep neural networks have been recently demonstrated to be vulnerable to backdoor attacks. Specifically, by altering a small set of training examples, an adversary can install a backdoor that is able to be used during inference to fully control the model's behavior. While the attack is very powerful, it crucially relies on the adversary being able to introduce arbitrary, often clearly mislabeled, inputs to the training set and can thus be foiled even by fairly rudimentary data sanitization. In this paper, we introduce a new approach to executing backdoor attacks. This approach utilizes adversarial examples and GAN-generated data. The key feature is that the resulting poisoned inputs appear to be consistent with their label and thus seem benign even upon human inspection.

Modulated Variational Auto-Encoders for Many-to-Many Musical Timbre Transfer    

tl;dr The paper uses Variational Auto-Encoding and network conditioning for Musical Timbre Transfer, we develop and generalize our architecture for many-to-many instrument transfers together with visualizations and evaluations.

Generative models have been successfully applied to image style transfer and domain translation. However, there is still a wide gap in the quality of results when learning such tasks on musical audio. Furthermore, most translation models only enable one-to-one or one-to-many transfer by relying on separate encoders or decoders and complex, computationally-heavy models. In this paper, we introduce the Modulated Variational auto-Encoders (MoVE) to perform musical timbre transfer. First, we define timbre transfer as applying parts of the auditory properties of a musical instrument onto another. We show that we can achieve and improve this task by conditioning existing domain translation techniques with Feature-wise Linear Modulation (FiLM). Then, by replacing the usual adversarial translation criterion by a Maximum Mean Discrepancy (MMD) objective, we alleviate the need for an auxiliary pair of discriminative networks. This allows a faster and more stable training, along with a controllable latent space encoder. By further conditioning our system on several different instruments, we can generalize to many-to-many transfer within a single variational architecture able to perform multi-domain transfers. Our models map inputs to 3-dimensional representations, successfully translating timbre from one instrument to another and supporting sound synthesis on a reduced set of control parameters. We evaluate our method in reconstruction and generation tasks while analyzing the auditory descriptor distributions across transferred domains. We show that this architecture incorporates generative controls in multi-domain transfer, yet remaining rather light, fast to train and effective on small datasets.

Local Image-to-Image Translation via Pixel-wise Highway Adaptive Instance Normalization    

No tl;dr =[

Recently, image-to-image translation has seen a significant success. Among them, image translation based on an exemplar image, which contains the target style information, has been popular, owing to its capability to handle multimodality as well as its suitability for practical use. However, most of the existing methods extract the style information from an entire exemplar and apply it to the entire input image, which introduces excessive image translation in irrelevant image regions. In response, this paper proposes a novel approach that jointly extracts out the local masks of the input image and the exemplar as targeted regions to be involved for image translation. In particular, the main novelty of our model lies in (1) co-segmentation networks for local mask generation and (2) the local mask-based highway adaptive instance normalization technique. We demonstrate the quantitative and the qualitative evaluation results to show the advantages of our proposed approach. Finally, our code is available at


tl;dr We introduce a new representation learning model, namely “Sample-Ensemble Genetic Evolutionary Network” (SEGEN), which can serve as an alternative approach to deep learning models.

Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years. With multiple hidden layers, deep learning models aim at computing the hierarchical feature representations of the observational data. Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning costs and the lack of result explainability, deep learning has also suffered from lots of criticism. In this paper, we will introduce a new representation learning model, namely “Sample-Ensemble Genetic Evolutionary Network” (SEGEN), which can serve as an alternative approach to deep learning models. Instead of building one single deep model, based on a set of sampled sub-instances, SEGEN adopts a genetic-evolutionary learning strategy to build a group of unit models generations by generations. The unit models incorporated in SEGEN can be either traditional machine learning models or the recent deep learning models with a much “narrower” and “shallower” architecture. The learning results of each instance at the final generation will be effectively combined from each unit model via diffusive propagation and ensemble learning strategies. From the computational perspective, SEGEN requires far less data, fewer computational resources and parameter tuning efforts, but has sound theoretic interpretability of the learning process and results. Extensive experiments have been done on several different real-world benchmark datasets, and the experimental results obtained by SEGEN have demonstrated its advantages over the state-of-the-art representation learning models.

On the loss landscape of a class of deep neural networks with no bad local valleys    

No tl;dr =[

We identify a class of over-parameterized deep neural networks with standard activation functions and cross-entropy loss which provably have no bad local valley, in the sense that from any point in parameter space there exists a continuous path on which the cross-entropy loss is non-increasing and gets arbitrarily close to zero. This implies that these networks have no sub-optimal strict local minima.

Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles    

tl;dr Adversarial training of ensembles provides robustness to adversarial examples beyond that observed in adversarially trained models and independently-trained ensembles thereof.

While deep learning has led to remarkable results on a number of challenging problems, researchers have discovered a vulnerability of neural networks in adversarial settings, where small but carefully chosen perturbations to the input can make the models produce extremely inaccurate outputs. This makes these models particularly unsuitable for safety-critical application domains (e.g. self-driving cars) where robustness is extremely important. Recent work has shown that augmenting training with adversarially generated data provides some degree of robustness against test-time attacks. In this paper we investigate how this approach scales as we increase the computational budget given to the defender. We show that increasing the number of parameters in adversarially-trained models increases their robustness, and in particular that ensembling smaller models while adversarially training the entire ensemble as a single model is a more efficient way of spending said budget than simply using a larger single model. Crucially, we show that it is the adversarial training of the ensemble, rather than the ensembling of adversarially trained models, which provides robustness.

State-Denoised Recurrent Neural Networks    

tl;dr We propose a mechanism for denoising the internal state of an RNN to improve generalization performance.

Recurrent neural networks (RNNs) are difficult to train on sequence processing tasks, not only because input noise may be amplified through feedback, but also because any inaccuracy in the weights has similar consequences as input noise. We describe a method for denoising the hidden state during training to achieve more robust representations thereby improving generalization performance. Attractor dynamics are incorporated into the hidden state to `clean up' representations at each step of a sequence. The attractor dynamics are trained through an auxillary denoising loss to recover previously experienced hidden states from noisy versions of those states. This state-denoised recurrent neural network (SDRNN) performs multiple steps of internal processing for each external sequence step. On a range of tasks, we show that the SDRNN outperforms a generic RNN as well as a variant of the SDRNN with attractor dynamics on the hidden state but without the auxillary loss. We argue that attractor dynamics---and corresponding connectivity constraints---are an essential component of the deep learning arsenal and should be invoked not only for recurrent networks but also for improving deep feedforward nets and intertask transfer.

A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing    

No tl;dr =[

One class anomaly detection on high-dimensional data is one of the critical issue in both fundamental machine learning research area and manufacturing applica- tions. A good anomaly detection should accurately discriminate anomalies from normal data. Although most previous anomaly detection methods achieve good performances, they do not perform well on high-dimensional imbalanced data- set 1) with a limited amount of data; 2) multi-modal distribution; 3) few anomaly data. In this paper, we develop a multi-modal one-class generative adversarial net- work based detector (MMOC-GAN) to distinguish anomalies from normal data (products). Apart from a domain-specific feature extractor, our model leverage a generative adversarial network(GAN). The generator takes in a modified noise vector using a pseudo latent prior and generate samples at the low-density area of the given normal data to simulate the anomalies. The discriminator then is trained to distinguish the generate samples from the normal samples. Since the generated samples simulate the low density area for each modal, the discriminator could directly detect anomalies from normal data. Experiments demonstrate that our model outperforms the state-of-the-art one-class classification models and other anomaly detection methods on both normal data and anomalies accuracy, as well as the F1 score. Also, the generated samples can fully capture the low density area of different types of products.

Towards Understanding Regularization in Batch Normalization    

No tl;dr =[

Batch Normalization (BN) improves both convergence and generalization in training neural networks. This work understands these phenomena theoretically. We analyze BN by using a basic block of neural networks, consisting of a kernel layer, a BN layer, and a nonlinear activation function. This basic network helps us understand the impacts of BN in three aspects. First, by viewing BN as an implicit regularizer, BN can be decomposed into population normalization (PN) and gamma decay as an explicit regularization. Second, learning dynamics of BN and the regularization show that training converged with large maximum and effective learning rate. Third, generalization of BN is explored by using statistical mechanics. Experiments demonstrate that BN in convolutional neural networks share the same traits of regularization as the above analyses.

The Laplacian in RL: Learning Representations with Efficient Approximations    

tl;dr We propose a scalable method to approximate the eigenvectors of the Laplacian in the reinforcement learning context and we show that the learned representations can improve the performance of an RL agent.

The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph. In reinforcement learning (RL), where the weighted graph may be interpreted as the state transition process induced by a behavior policy acting on the environment, approximating the eigenvectors of the Laplacian provides a promising approach to state representation learning. However, existing methods for performing this approximation are ill-suited in general RL settings for two main reasons: First, they are computationally expensive, often requiring operations on large matrices. Second, these methods lack adequate justification beyond simple, tabular, finite-state settings. In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context. We systematically evaluate our approach and empirically show that it generalizes beyond the tabular, finite-state setting. Even in tabular, finite-state settings, its ability to approximate the eigenvectors outperforms previous proposals. Finally, we show the potential benefits of using a Laplacian representation learned using our method in goal-achieving RL tasks, providing evidence that our technique can be used to significantly improve the performance of an RL agent.


No tl;dr =[

Recent work on critical initializations of deep neural networks has shown that by constraining the spectrum of input-output Jacobians allows for fast training of very deep networks without skip connections. The current understanding of this class of initializations is limited with respect to classical notions from optimization. In particular, the connections between Jacobian eigenvalues and curvature of the parameter space are unknown. Similarly, there is no firm understanding of the effects of maintaining orthogonality during training. With this work we complement the existing understanding of critical initializations and show that the curvature is proportional to the maximum singular value of the Jacobian. Furthermore we show that optimization under orthogonality constraints ameliorates the dependence on choice of initial parameters, but is not strictly necessary.

Predicting the Generalization Gap in Deep Networks with Margin Distributions    

tl;dr We develop a new scheme to predict the generalization gap in deep networks with high accuracy.

As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functions such as cross-entropy are not a reliable indicator of generalization. This leads to the crucial question of how generalization gap should be predicted from the training data and network parameters. In this paper, we propose such a measure, and conduct extensive empirical studies on how well it can predict the generalization gap. Our measure is based on the concept of margin distribution, which are the distances of training points to the decision boundary. We find that it is necessary to use margin distributions at multiple layers of a deep network. On the CIFAR-10 and the CIFAR-100 datasets, our proposed measure correlates very strongly with the generalization gap. In addition, we find the following other factors to be of importance: normalizing margin values for scale independence, using characterizations of margin distribution rather than just the margin (closest distance to decision boundary), and working in log space instead of linear space (effectively using a product of margins rather than a sum). Our measure can be easily applied to feedforward deep networks with any architecture and may point towards new training loss functions that could enable better generalization.

Adversarial Imitation via Variational Inverse Reinforcement Learning    

tl;dr Our proposed method builds on GANs and exploits potential-based reward shaping to learn near-optimal rewards and policies from expert demonstrations.

We consider a problem of learning a reward and policy from expert examples under unknown dynamics in high-dimensional scenarios. Our proposed method builds on the framework of generative adversarial networks and exploits reward shaping to learn near-optimal rewards and policies. Potential-based reward shaping functions are known to guide the learning agent whereas in this paper we bring forward their benefits in learning near-optimal rewards. Our method simultaneously learns a potential-based reward shaping function through variational information maximization along with the reward and policy under the adversarial learning formulation. We evaluate our method on various high-dimensional complex control tasks. We also evaluate our learned rewards in transfer learning problems where training and testing environments are made to be different from each other in terms of dynamics or structure. Our experimentation shows that our proposed method not only learns near-optimal rewards and policies matching expert behavior, but also performs significantly better than state-of-the-art inverse reinforcement learning algorithms.

EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models    

tl;dr We introduced entropy maximization to GANs, leading to a reinterpretation of the critic as an energy function.

Unsupervised learning is about capturing dependencies between variables and is driven by the contrast between the probable vs improbable configurations of these variables, often either via a generative model which only samples probable ones or with an energy function (unnormalized log-density) which is low for probable ones and high for improbable ones. Here we consider learning both an energy function and an efficient approximate sampling mechanism for the corresponding distribution. Whereas the critic (or discriminator) in generative adversarial networks (GANs) learns to separate data and generator samples, introducing an entropy maximization regularizer on the generator can turn the interpretation of the critic into an energy function, which separates the training distribution from everything else, and thus can be used for tasks like anomaly or novelty detection. This paper is motivated by the older idea of sampling in latent space rather than data space because running a Monte-Carlo Markov Chain (MCMC) in latent space has been found to be easier and more efficient, and because a GAN-like generator can convert latent space samples to data space samples. For this purpose, we show how a Markov chain can be run in latent space whose samples can be mapped to data space, producing better samples. These samples are also used for the negative phase gradient required to estimate the log-likelihood gradient of the data space energy function. To maximize entropy at the output of the generator, we take advantage of recently introduced neural estimators of mutual information. We find that in addition to producing a useful scoring function for anomaly detection, the resulting approach produces sharp samples (like GANs) while covering the modes well, leading to high Inception and Fréchet scores.

Ergodic Measure Preserving Flows    

tl;dr A novel computational scalable inference framework for training deep generative models and general statistical inference.

Training probabilistic models with neural network components is intractable in most cases and requires to use approximations such as Markov chain Monte Carlo (MCMC), which is not scalable and requires significant hyper-parameter tuning, or mean-field variational inference (VI), which is biased. While there has been attempts at combining both approaches, the resulting methods have some important limitations in theory and in practice. As an alternative, we propose a novel method which is scalable, like mean-field VI, and, due to its theoretical foundation in ergodic theory, is also asymptotically accurate, like MCMC. We test our method on popular benchmark problems with deep generative models and Bayesian neural networks. Our results show that we can outperform existing approximate inference methods.


tl;dr We study the behavior of weight-tied multilayer vanilla autoencoders under the assumption of random weights. Via an exact characterization in the limit of large dimensions, our analysis reveals interesting phase transition phenomena.

We study the behavior of weight-tied multilayer vanilla autoencoders under the assumption of random weights. Via an exact characterization in the limit of large dimensions, our analysis reveals interesting phase transition phenomena when the depth becomes large. This, in particular, provides quantitative answers and insights to three questions that were yet fully understood in the literature. Firstly, we provide a precise answer on how the random deep weight-tied autoencoder model performs “approximate inference” as posed by Scellier et al. (2018), and its connection to reversibility considered by several theoretical studies. Secondly, we show that deep autoencoders display a higher degree of sensitivity to perturbations in the parameters, distinct from the shallow counterparts. Thirdly, we obtain insights on pitfalls in training initialization practice, and demonstrate experimentally that it is possible to train a deep autoencoder, even with the tanh activation and a depth as large as 200 layers, without resorting to techniques such as layer-wise pre-training or batch normalization. Our analysis is not specific to any depths or any Lipschitz activations, and our analytical techniques may have broader applicability.

Self-Binarizing Networks    

tl;dr A method to binarize both weights and activations of a deep neural network that is efficient in computation and memory usage and performs better than the state-of-the-art.

We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary. To obtain similar binary networks, existing methods rely on the sign activation function. This function, however, has no gradients for non-zero values, which makes standard backpropagation impossible. To circumvent the difficulty of training a network relying on the sign activation function, these methods alternate between floating-point and binary representations of the network during training, which is sub-optimal and inefficient. We approach the binarization task by training on a unique representation involving a smooth activation function, which is iteratively sharpened during training until it becomes a binary representation equivalent to the sign activation function. Additionally, we introduce a new technique to perform binary batch normalization that simplifies the conventional batch normalization by transforming it into a simple comparison operation. This is unlike existing methods, which are forced to the retain the conventional floating-point-based batch normalization. Our binary networks, apart from displaying advantages of lower memory and computation as compared to conventional floating-point and binary networks, also show higher classification accuracy than existing state-of-the-art methods on multiple benchmark datasets.

Learning Mixed-Curvature Representations in Product Spaces    

tl;dr Product manifold embedding spaces with heterogenous curvature yield improved representations compared to traditional embedding spaces for a variety of structures.

The quality of the representations achieved by embeddings is determined by how well the geometry of the embedding space matches the structure of the data. Euclidean space has been the workhorse space for embeddings; recently hyperbolic and spherical spaces are gaining popularity due to their ability to better embed new types of structured data---such as hierarchical data---but most data is not structured so uniformly. We address this problem by proposing embedding into a product manifold combining multiple copies of spherical, hyperbolic, and Euclidean spaces, providing a space of heterogeneous curvature suitable for a wide variety of structures. We introduce a heuristic to estimate the sectional curvature of graph data and directly determine the signature---the number of component spaces and their dimensions---of the product manifold. Empirically, we jointly learn the curvature and the embedding in the product space via Riemannian optimization. We discuss how to define and compute intrinsic quantities such as means---a challenging notion for product manifolds---and provably learnable optimization functions. On a range of datasets and reconstruction tasks, our product space embeddings outperform single Euclidean or hyperbolic spaces used in previous works, reducing distortion by 32.55% on a Facebook social network dataset. We learn word embeddings and find that a product of hyperbolic spaces in 50 dimensions consistently improves on baseline Euclidean and hyperbolic embeddings by 2.6 points in Spearman rank correlation on similarity tasks and 3.4 points on analogy accuracy.

Large-Scale Visual Speech Recognition    

No tl;dr =[

This work presents a scalable solution to open-vocabulary visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video). In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words. The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set. In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information. Our approach significantly improves on other lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.

Learned optimizers that outperform on wall-clock and validation loss    

tl;dr We analyze problems when training learned optimizers, address those problems via variational optimization using two complementary gradient estimators, and train optimizers that are 5x faster in wall-clock time than baseline optimizers (e.g. Adam).

Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned update functions may similarly outperform current hand-designed optimizers, especially for specific tasks. However, learned optimizers are notoriously difficult to train and have yet to demonstrate wall-clock speedups over hand-designed optimizers, and thus are rarely used in practice. Typically, learned optimizers are trained by truncated backpropagation through an unrolled optimization process. The resulting gradients are either strongly biased (for short truncations) or have exploding norm (for long truncations). In this work we propose a training scheme which overcomes both of these difficulties, by dynamically weighting two unbiased gradient estimators for a variational loss on optimizer performance. This allows us to train neural networks to perform optimization faster than well tuned first-order methods. Moreover, by training the optimizer against validation loss, as opposed to training loss, we are able to use it to train models which generalize better than those trained by first order methods. We demonstrate these results on problems where our learned optimizer trains convolutional networks in a fifth of the wall-clock time compared to tuned first-order methods, and with an improvement

StrokeNet: A Neural Painting Environment    

tl;dr StrokeNet is a novel architecture where the agent is trained to draw by strokes on a differentiable simulation of the environment, which could effectively exploit the power of back-propagation.

We've seen tremendous success of image generating models these years. Generating an image through a neural network is like ``dreaming'', which is fundamentally different from how humans create artwork using brushes. To imitate human drawing, interactions between the agent and the environment is required to allow trials from the agent. However, the environment is usually non-differentiable, leading to slow convergence and massive computation. In this paper we try to address the discrete nature of software environment with an intermediate, differentiable simulation, which can be interpreted as a neural perception of the surroundings of the upper agent. We present StrokeNet, a novel model where the agent is trained upon a well-crafted neural approximation of the painting environment. With this approach, our agent was able to learn to write characters such as MNIST digits very quickly in an unsupervised manner. Our primary contribution is the neural simulation of real-world environment. Furthermore, the agent trained with our approach can be directly transferred to real world with learned skills. To the best of our knowledge, StrokeNet is the first model to apply differentiable simulation to real-world learning problems and standard datasets.

Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation    

tl;dr We prove that the mode collapse in conditional GANs is largely attributed to a mismatch between reconstruction loss and GAN loss and introduce a set of novel loss functions as alternatives for reconstruction loss.

Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, are largely contributed by the success of conditional GAN models, which are often optimized by the joint use of the GAN loss with the reconstruction loss. However, we show that this training recipe shared by almost all existing methods always leads to a suboptimal generator and has one critical side effect: lack of diversity in output samples. In order to accomplish both training stability and multimodal output generation, we propose novel training schemes with a new set of losses that simply replace the reconstruction loss, and thus applicable to any conditional generation tasks. We show this by performing thorough experiments on image-to-image translation, super-resolution, and image inpainting tasks with Cityscapes, and CelebA dataset. A quantitative evaluation also confirms that our methods achieve great diversity of outputs while retaining or even improving the quality of images.

Confidence Calibration in Deep Neural Networks through Stochastic Inferences    

tl;dr We propose a framework to learn confidence-calibrated networks by designing a novel loss function that incorporates predictive uncertainty estimated through stochastic inferences.

We propose a generic framework to calibrate accuracy and confidence (score) of a prediction through stochastic inferences in deep neural networks. We first analyze relation between variation of multiple model parameters for a single example inference and variance of the corresponding prediction scores by Bayesian modeling of stochastic regularization. Our empirical observation shows that accuracy and score of a prediction are highly correlated with variance of multiple stochastic inferences given by stochastic depth or dropout. Motivated by these facts, we design a novel variance-weighted confidence-integrated loss function that is composed of two cross-entropy loss terms with respect to ground-truth and uniform distribution, which are balanced by variance of stochastic prediction scores. The proposed loss function enables us to learn deep neural networks that predict confidence calibrated scores using a single inference. Our algorithm presents outstanding confidence calibration performance and improves classification accuracy with two popular stochastic regularization techniques---stochastic depth and dropout---in multiple models and datasets; it alleviates overconfidence issue in deep neural networks significantly by training networks to achieve prediction accuracy proportional to confidence of prediction.

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations    

tl;dr We propose ImageNet-C to measure classifier corruption robustness and ImageNet-P to measure perturbation robustness

In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize.

Batch Normalization Sampling    

tl;dr We propose accelerating Batch Normalization (BN) through sampling less correlated data for reduction operations with regular execution pattern, which achieves up to 2x and 20% speedup for BN itself and the overall training, respectively.

Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reduction operations. In this paper, we propose alleviating this problem through sampling only a small fraction of data for normalization at each iteration. Specifically, we model it as a statistical sampling problem and identify that by sampling less correlated data, we can largely reduce the requirement of the number of data for statistics estimation in BN, which directly simplifies the reduction operations. Based on this conclusion, we propose two sampling strategies, “Batch Sampling” (randomly select several samples from each batch) and “Feature Sampling” (randomly select a small patch from each feature map of all samples), that take both computational efficiency and sample correlation into consideration. Furthermore, we introduce an extremely simple variant of BN, termed as Virtual Dataset Normalization (VDN), that can normalize the activations well with few synthetical random samples. All the proposed methods are evaluated on various datasets and networks, where an overall training speedup by up to 20% on GPU is practically achieved without the support of any specialized libraries, and the loss on accuracy and convergence rate are negligible. Finally, we extend our work to the “micro-batch normalization” problem and yield comparable performance with existing approaches at the case of tiny batch size.

Multi-Scale Stacked Hourglass Network for Human Pose Estimation    

tl;dr Differentiated inputs cause functional differentiation of the network, and the interaction of loss functions between networks can affect the optimization process.

Stacked hourglass network has become an important model for Human pose estimation. The estimation of human body posture depends on the global information of the keypoints type and the local information of the keypoints location. The consistent processing of inputs and constraints makes it difficult to form differentiated and determined collaboration mechanisms for each stacked hourglass network. In this paper, we propose a Multi-Scale Stacked Hourglass (MSSH) network to high-light the differentiation capabilities of each Hourglass network for human pose estimation. The pre-processing network forms feature maps of different scales,and dispatch them to various locations of the stack hourglass network, where the small-scale features reach the front of stacked hourglass network, and large-scale features reach the rear of stacked hourglass network. And a new loss function is proposed for multi-scale stacked hourglass network. Different keypoints have different weight coefficients of loss function at different scales, and the keypoints weight coefficients are dynamically adjusted from the top-level hourglass network to the bottom-level hourglass network. Experimental results show that the pro-posed method is competitive with respect to the comparison algorithm on MPII and LSP datasets.

On Regularization and Robustness of Deep Neural Networks    

No tl;dr =[

Despite their success, deep neural networks suffer from several drawbacks: they lack robustness to small changes of input data known as "adversarial examples" and training them with small amounts of annotated data is challenging. In this work, we study the connection between regularization and robustness by viewing neural networks as elements of a reproducing kernel Hilbert space (RKHS) of functions and by regularizing them using the RKHS norm. Even though this norm cannot be computed, we consider various approximations based on upper and lower bounds. These approximations lead to new strategies for regularization, but also to existing ones such as spectral norm penalties or constraints, gradient penalties, or adversarial training. Besides, the kernel framework allows us to obtain margin-based bounds on adversarial generalization. We study the obtained algorithms for learning on small datasets, learning adversarially robust models, and discuss implications for learning implicit generative models.

Principled Deep Neural Network Training through Linear Programming    

tl;dr Using linear programming we show that the computational complexity of approximate Deep Neural Network training depends polynomially on the data size for several architectures

Deep Learning has received significant attention due to its impressive performance in many state-of-the-art learning tasks. Unfortunately, while very powerful, Deep Learning is not well understood theoretically and in particular only recently results for the complexity of training deep neural networks have been obtained. In this work we show that large classes of deep neural networks with various architectures (e.g., DNNs, CNNs, Binary Neural Networks, and ResNets), activation functions (e.g., ReLUs and leaky ReLUs), and loss functions (e.g., Hinge loss, Euclidean loss, etc) can be trained to near optimality with desired target accuracy using linear programming in time that is exponential in the size of the architecture and polynomial in the size of the data set; this is the best one can hope for due to the NP-Hardness of the problem and in line with previous work. In particular, we obtain polynomial time algorithms for training for a given fixed network architecture. Our work applies more broadly to empirical risk minimization problems which allows us to generalize various previous results and obtain new complexity results for previously unstudied architectures in the proper learning setting.

Learning Unsupervised Learning Rules    

tl;dr We learn an unsupervised learning algorithm that produces useful representations from a set of supervised tasks. At test-time, we apply this algorithm to new tasks without any supervision and show performance comparable to a VAE.

A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this goal is approached by minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise incidentally. In this work, we propose instead to directly target a later desired task by meta-learning an unsupervised learning rule, which leads to representations useful for that task. Here, our desired task (meta-objective) is the performance of the representation on semi-supervised classification, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations that perform well under this meta-objective. Additionally, we constrain our unsupervised update rule to a be a biologically-motivated, neuron-local function, which enables it to generalize to novel neural network architectures. We show that the meta-learned update rule produces useful features and sometimes outperforms existing unsupervised learning techniques. We further show that the meta-learned unsupervised update rule generalizes to train networks with different widths, depths, and nonlinearities. It also generalizes to train on data with randomly permuted input dimensions and even generalizes from image datasets to a text task.

Learning Recurrent Binary/Ternary Weights    

tl;dr We propose high-performance LSTMs with binary/ternary weights, that can greatly reduce implementation complexity

Recurrent neural networks (RNNs) have shown excellent performance in processing sequence data. However, they are both complex and memory intensive due to their recursive nature. These limitations make RNNs difficult to embed on mobile devices requiring real-time processes with limited hardware resources. To address the above issues, we introduce a method that can learn binary and ternary weights during the training phase to facilitate hardware implementations of RNNs. As a result, using this approach replaces all multiply-accumulate operations by simple accumulations, bringing significant benefits to custom hardware in terms of silicon area and power consumption. On the software side, we evaluate the performance (in terms of accuracy) of our method using long short-term memories (LSTMs) on various sequential models including sequence classification and language modeling. We demonstrate that our method achieves competitive results on the aforementioned tasks while using binary/ternary weights during the runtime. On the hardware side, we present custom hardware for accelerating the recurrent computations of LSTMs with binary/ternary weights. Ultimately, we show that LSTMs with binary/ternary weights can achieve up to 12x memory saving and 10x inference speedup compared to the full-precision implementation on an ASIC platform.

Associate Normalization    

No tl;dr =[

Normalization is a key technique for training deep neural networks. It improves the stability of the training process and thus makes the networks easier to train. However, in typical normalization methods, the rescaling parameters that control the mean and variance of the output do not associate with any input information during the forward phase. Therefore, inputs of different types are treated as from the exact same distribution, which may limit the feature expressiveness of normalization module. We present Associate Normalization (AssocNorm) to overcome the above limitation. AssocNorm extracts the key information from input features and connects them with rescaling parameters by an auto-encoder-like neural network in the normalization module. Furthermore, AssocNorm normalizes the features of each example individually, so the accuracy is relatively stable for different batch sizes. The experimental results show that AssocNorm achieves better performance than Batch Normalization on several benchmark datasets under various hyper-parameter settings.

Residual Non-local Attention Networks for Image Restoration    

tl;dr New state-of-the-art framework for image restoration

In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with soft attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. We demonstrate the effectiveness of our proposed method for various image restoration tasks, including image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments show that our method achieves comparable or better results compared with recently leading methods.

An experimental study of layer-level training speed and its impact on generalization    

tl;dr This paper provides empirical evidence that 1) the speed at which each layer trains influences generalization and 2) this phenomenon is at the root of weight decay's and adaptive gradient methods' impact on generalization.

How optimization influences the generalization ability of a DNN is still an active area of research. This work aims to unveil and study a factor of influence: we show that the speed at which each layer trains, measured by the rotation rate of each layer's weight vector (or layer rotation rate), has a consistent and substantial impact on generalization. We develop a visualization technique and an optimization algorithm to monitor and control the layer rotation rates during training, and show across multiple tasks and training settings that rotating all the layers' weights synchronously and at high rate repeatedly induces the best generalization performance. Going further, our experiments suggest that weight decay is an essential ingredient for inducing such beneficial layer rotation rates with SGD, and that the impact of adaptive gradient methods on training speed and generalization is solely due to the modifications they induce to each layer's training speed compared to SGD. Besides these fundamental findings, we also expect that the tools we introduce will reduce the meta-parameter tuning required to get the best generalization out of a deep network.

Universal Marginalizer for Amortised Inference and Embedding of Generative Models    

No tl;dr =[

Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty. There exist a considerable number of methods for performing inference in probabilistic graphical models, however, they can be computational costly due to significant time burden, storage requirements or they lack theoretical guarantees of convergence and accuracy when applied to very large graphical models. We propose the Universal Marginaliser Importance Sampler (UM-IS) -- a hybrid inference scheme that combines the flexibility of a deep neural network trained on samples from the model and it inherits the asymptotic guarantees of importance sampling. We show how combining samples drawn from the graphical model with an appropriate masking function allows us to train a single neural network to approximate any of the corresponding conditional marginal distributions, and thus amortise the cost of inference. We demonstrate that the efficiency of importance sampling is significantly improved by using as the proposal distribution samples from the neural network. We also use the embeddings obtained from the proposed neural network and utilise them for different tasks such as clustering, classification and interpretation of relationships between the nodes. Finally, we benchmark the method on a large graph (>1000 nodes), showing that UM-IS outperforms sampling-based based methods by a large margin while being computationally efficient.


tl;dr A deep learning based graph classification method plus a new adaptive method for graph pooling.

Most of the existing Graph Neural Networks (GNNs) are the mere extension of the Convolutional Neural Networks (CNNs) to graphs. Generally, they consist of several steps of message passing between the nodes followed by a global indiscriminate feature pooling function. However, most of the times the nodes are unlabeled or their labels (or the given feature vectors of the nodes) provide no information about the similarity between the nodes and the locations of the nodes in the graph. Accordingly, message passing may not propagate helpful information throughout the graph. We show that this conventional approach fails to learn to solve even simple graph classification tasks. We alleviate this serious shortcoming of the GNNs by making them a two step method where in the second step, the message passing block is given the continuous features obtained by the embedding algorithm in the first step. The GNN learns to solve the given task by inferring the topological structure of the graph encoded in the spatial distribution of the embedded vectors. The second challenge we address in this paper is designing a pooling algorithm applicable to graphs. We turn the problem of graph down-sampling into a column sampling problem, i.e., the sampling algorithm samples a subset of the nodes whose feature vectors preserve the spatial distribution of all the feature vectors. We apply the proposed approach to several established benchmark data sets and it is shown that the proposed geometrical approach strongly improves the state-of-the-art for several data-sets.

Learning shared manifold representation of images and attributes for generalized zero-shot learning    

No tl;dr =[

The most prior methods of zero-shot learning have realized predicting labels of unseen images by learning a mapping from images to pre-defined class-attributes. However, recent studies show that these approaches severely suffers from the issue of biased prediction under the more realistic generalized zero-shot learning (GZSL) scenarios, i.e., their classifier tends to predict all the examples from both seen and unseen class as one of the seen classes. The cause of this problem is that we can not obtain training data of the unseen class and that the representation of attributes is poor. To solve this, we propose a concept to learn a mapping that embeds both images and attributes to a space that is robust to such representations and generalized even for unseen data, which we refer to shared manifold learning. Furthermore, we propose modality invariant variational autoencoders, which can perform shared manifold learning by training variational autoencoders with both images and attributes as inputs. The empirical validation of well-known datasets in GZSL shows that our method achieves the significantly superior performances to the existing relation-based works.

Learning Heuristics for Automated Reasoning through Reinforcement Learning    

tl;dr RL finds better heuristics for automated reasoning algorithms.

We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning. We focus on backtracking search algorithms for quantified Boolean logics, which already can solve formulas of impressive size - up to 100s of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For challenging problems, the heuristic learned through our approach reduces execution time by >=90% compared to the existing handwritten heuristics.

Systematic Generalization: What Is Required and Can It Be Learned?    

tl;dr We show that modular structured models are the best in terms of systematic generalization and that their end-to-end versions don't generalize as well.

Numerous models for grounded language understanding have been recently proposed, including (i) generic modules that can be used easily adapted to any given task with little adaptation and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare generic and modular models in how much they lend themselves to a particular form of systematic generalization. Using a synthetic VQA test, we evaluate which models are capable of reasoning about all possible object pairs after training on only a small subset of them. Our findings show that the generalization of modular models is much more systematic and that it is highly sensitive to the module layout, i.e. to how exactly the modules are connected. We furthermore investigate if modular models that generalize well could be made more end-to-end by learning their layout and parametrization. We show how end-to-end methods from prior work often learn a wrong layout and a spurious parametrization that do not facilitate systematic generalization.

A Rate-Distortion Theory of Adversarial Examples    

tl;dr We suggest that rate-distortion theory precisely characterizes the accuracy versus robustness to adversarial examples trade-off

The generalization ability of deep neural networks (DNNs) is interwined with model complexity, robustness and capacity. We employ information theory to establish an equivalence between a DNN and a noisy communication channel, and obtain a notion of capacity that allows us characterize generalization behavior of DNNs for adversarial inputs.

Metropolis-Hastings view on variational inference and adversarial training    

tl;dr Learning to sample via lower bounding the acceptance rate of the Metropolis-Hastings algorithm

In this paper we propose to view the acceptance rate of the Metropolis-Hastings algorithm as a universal objective for learning to sample from target distribution -- given either as a set of samples or in the form of unnormalized density. This point of view unifies the goals of such approaches as Markov Chain Monte Carlo (MCMC), Generative Adversarial Networks (GANs), variational inference. To reveal the connection we derive the lower bound on the acceptance rate and treat it as the objective for learning explicit and implicit samplers. The form of the lower bound allows for doubly stochastic gradient optimization in case the target distribution factorizes (i.e. over data points). We empirically validate our approach on Bayesian inference for neural networks and generative models for images.

A Modern Take on the Bias-Variance Tradeoff in Neural Networks    

tl;dr We revisit empirically and theoretically the bias-variance tradeoff for neural networks to shed more light on their generalization properties.

We revisit the bias-variance tradeoff for neural networks in light of modern empirical findings. The traditional bias-variance tradeoff in machine learning suggests that as model complexity grows, variance increases. Classical bounds in statistical learning theory point to the number of parameters in a model as a measure of model complexity, which means the tradeoff would indicate that variance increases with the size of neural networks. However, we empirically find that variance due to training set sampling is roughly constant (with both width and depth) in practice. Variance caused by the non-convexity of the loss landscape is different. We find that it decreases with width and increases with depth, in our setting. We provide theoretical analysis, in a simplified setting inspired by linear models, that is consistent with our empirical findings for width. We view bias-variance as a useful lens to study generalization through and encourage further theoretical explanation from this perspective.

Set Transformer    

tl;dr Attention-based neural network to process set-structured data

Many machine learning tasks such as multiple instance learning, 3D shape recognition and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the permutation of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating increased performance compared to recent methods for set-structured data.

Recycling the discriminator for improving the inference mapping of GAN    

No tl;dr =[

Generative adversarial networks (GANs) have achieved outstanding success in generating the high-quality data. Focusing on the generation process, existing GANs learn a unidirectional mapping from the latent vector to the data. Later, various studies point out that the latent space of GANs is semantically meaningful and can be utilized in advanced data analysis and manipulation. In order to analyze the real data in the latent space of GANs, it is necessary to investigate the inverse generation mapping from the data to the latent vector. To tackle this problem, the bidirectional generative models introduce an encoder to establish the inverse path of the generation process. Unfortunately, this effort leads to the degradation of generation quality because the imperfect generator rather interferes the encoder training and vice versa. In this paper, we propose an effective algorithm to infer the latent vector based on existing unidirectional GANs by preserving their generation quality. It is important to note that we focus on increasing the accuracy and efficiency of the inference mapping but not influencing the GAN performance (i.e., the quality or the diversity of the generated sample). Furthermore, utilizing the proposed inference mapping algorithm, we suggest a new metric for evaluating the GAN models by measuring the reconstruction error of unseen real data. The experimental analysis demonstrates that the proposed algorithm achieves more accurate inference mapping than the existing method and provides the robust metric for evaluating GAN performance.

On Self Modulation for Generative Adversarial Networks    

tl;dr A simple GAN modification that improves performance across many losses, architectures, regularization schemes, and datasets.

Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance across different data sets, architectures, losses, regularizers, and hyperparameter settings. Intuitively, self-modulation allows the intermediate feature maps of a generator to change as a function of the input noise vector. While reminiscent of other conditioning techniques, it requires no labeled data. In a large-scale empirical study we observe a relative decrease of 5%-35% in FID. Furthermore, all else being equal, adding this modification to the generator leads to improved performance in 124/144 (86%) of the studied settings. Self-modulation is a simple architectural change that requires no additional parameter tuning, which suggests that it can be applied readily to any GAN.

Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks    

tl;dr A new nonlinear defense against adversarial attacks.

The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, in this paper, we propose a nonlinear radial basis convolutional feature transformation by learning the Mahalanobis distance function that maps the input convolutional features from the same class into tight clusters. In such a space, the clusters become compact and well-separated, which prevent small adversarial perturbations from forcing a sample to cross the decision boundary. We test the proposed method on three publicly available image classification and segmentation data-sets namely, MNIST, ISBI ISIC skin lesion, and NIH ChestX-ray14. We evaluate the robustness of our method to different gradient (targeted and untargeted) and non-gradient based attacks and compare it to several non-gradient masking defense strategies. Our results demonstrate that the proposed method can boost the performance of deep convolutional neural networks against adversarial perturbations without accuracy drop on clean data.


tl;dr A novel marginalized average attentional network for weakly-supervised temporal action localization

In weakly-supervised temporal action localization, previous works suffer from overestimating the most salient regions and fail to locate dense and integral regions for each entire action. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples the subsets from the video snippet features based on the latent discriminative probabilities and takes the expectation over all the subset features. Theoretically, we prove that the learned latent discriminative probabilities reduce the difference of responses between the most salient regions and the others, and thus MAAN generates better class activation sequences to identify more dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from $O(2^T)$ to $O(T^2)$. Extensive experiments on two large-scale video datasets show that our MAAN achieves superior performance on weakly-supervised temporal action localization task.

Approximation and non-parametric estimation of ResNet-type convolutional neural networks via block-sparse fully-connected neural networks    

tl;dr It is shown that ResNet-type CNNs are a universal approximator and its expression ability is not worse than fully connected neural networks (FNNs) with a \textit{block-sparse} structure even if the size of each layer in the CNN is fixed.

We develop new approximation and statistical learning theories of convolutional neural networks (CNNs) via the ResNet-type structure where the channel size, width, and filter size are fixed. It is shown that a ResNet-type CNN is a universal approximator and its expression ability is no worse than fully connected neural networks (FNNs) with a \textit{block-sparse} structure even if the size of each layer in the CNN is fixed. Our result is general in the sense that we can automatically translate any approximation rate achieved by block-sparse FNNs into that by CNNs. Thanks to the general theory, it is shown that learning on CNNs satisfies optimality in approximation and estimation of several important function classes. As applications, we consider two types of function classes to be estimated: the Barron class and the H\"older class. We prove the regularized empirical risk minimization (ERM) estimator can achieve the same rate as FNNs even the channel size, filter size, and width of CNNs are constant with respect to the sample size. This is minimax optimal (up to logarithmic factors) for the H\"older class. Our proof is based on sophisticated evaluations of the covering number of CNNs and the non-trivial parameter rescaling technique to control the Lipschitz constant of CNNs to be constructed.

Super-Resolution via Conditional Implicit Maximum Likelihood Estimation    

tl;dr We propose a new method for image super-resolution based on IMLE.

Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an alternative approach based on an extension of the method of Implicit Maximum Likelihood Estimation (IMLE). We demonstrate greater effectiveness at noise reduction and preservation of the original colours and shapes, yielding more realistic super-resolved images.

Improved robustness to adversarial examples using Lipschitz regularization of the loss    

tl;dr Improvements to adversarial robustness, as well as provable robustness guarantees, are obtained by augmenting adversarial training with a tractable Lipschitz regularization

Adversarial training is an effective method for improving robustness to adversarial attacks. We show that adversarial training using the Fast Signed Gradient Method can be interpreted as a form of regularization. We implemented a more effective form of adversarial training, which in turn can be interpreted as regularization of the loss in the 2-norm, $\|\nabla_x \ell(x)\|_2$. We obtained further improvements to adversarial robustness, as well as provable robustness guarantees, by augmenting adversarial training with Lipschitz regularization.

Towards GAN Benchmarks Which Require Generalization    

tl;dr We argue that GAN benchmarks must require a large sample from the model to penalize memorization and investigate whether neural network divergences have this property.

For many evaluation metrics commonly used as benchmarks for unconditional image generation, trivially memorizing the training set attains a better score than models which are considered state-of-the-art. We clarify a necessary condition for an evaluation metric not to behave this way: estimating the function must require a large sample from the model. In search of such a metric, we turn to neural network divergences (NNDs), which are defined in terms of a neural network trained to distinguish between distributions. These metrics cannot be "solved" by training set memorization, while still being perceptually correlated and computationally tractable. We survey past work on using NNDs for evaluation and implement an example black-box metric based on these ideas. Through experimental validation we show that it can effectively measure diversity, sample quality, and generalization.

Estimating Information Flow in DNNs    

tl;dr Deterministic deep neural networks do not discard information, but they do cluster their inputs.

We study the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory. The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in which the mutual information I(X;T) between the input X and internal representations T decreases. Several papers observe compression of estimated mutual information on different DNN models, but the true I(X;T) over these networks is provably either constant (discrete X) or infinite (continuous X). This work explains the discrepancy between theory and experiments, and clarifies what was actually measured by these past works. To this end, we introduce an auxiliary (noisy) DNN framework for which I(X;T) is a meaningful quantity that depends on the network's parameters. This noisy framework is shown to be a good proxy for the original (deterministic) DNN both in terms of performance and the learned representations. We then develop a rigorous estimator for I(X;T) in noisy DNNs and observe compression in various models. By relating I(X;T) in the noisy DNN to an information-theoretic communication problem, we show that compression is driven by the progressive clustering of hidden representations of inputs from the same class. Several methods to directly monitor clustering of hidden representations, both in noisy and deterministic DNNs, are used to show that meaningful clusters form in the T space. Finally, we return to the estimator of I(X;T) employed in past works, and demonstrate that while it fails to capture the true (vacuous) mutual information, it does serve as a measure for clustering. This clarifies the past observations of compression and isolates the geometric clustering of hidden representations as the true phenomenon of interest.

Meta-Learning Probabilistic Inference for Prediction    

tl;dr Novel framework for meta-learning that unifies and extends a broad class of existing few-shot learning methods. Achieves strong performance on few-shot learning benchmarks without requiring iterative test-time inference.

This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce \Versa{}, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. \Versa{} substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate \Versa{} on benchmark datasets where the method sets new state-of-the-art results, and can handle arbitrary number of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.

Provable Guarantees on Learning Hierarchical Generative Models with Deep CNNs    

tl;dr A generative model for deep CNNs with provable theoretical guarantees that actually works

Learning deep networks is computationally hard in the general case. To show any positive theoretical results, one must make assumptions on the data distribution. Current theoretical works often make assumptions that are very far from describing real data, like sampling from Gaussian distribution or linear separability of the data. We describe an algorithm that learns convolutional neural network, assuming the data is sampled from a deep generative model that generates images level by level, where lower resolution images correspond to latent semantic classes. We analyze the convergence rate of our algorithm assuming the data is indeed generated according to this model (as well as additional assumptions). While we do not pretend to claim that the assumptions are realistic for natural images, we do believe that they capture some true properties of real data. Furthermore, we show that on CIFAR-10, the algorithm we analyze achieves results in the same ballpark with vanilla convolutional neural networks that are trained with SGD.

Deep reinforcement learning with relational inductive biases    

tl;dr Relational inductive biases improve out-of-distribution generalization capacities in model-free reinforcement learning agents

We introduce an approach for augmenting model-free deep reinforcement learning agents with a mechanism for relational reasoning over structured representations, which improves performance, learning efficiency, generalization, and interpretability. Our architecture encodes an image as a set of vectors, and applies an iterative message-passing procedure to discover and reason about relevant entities and relations in a scene. In six of seven StarCraft II Learning Environment mini-games, our agent achieved state-of-the-art performance, and surpassed human grandmaster-level on four. In a novel navigation and planning task, our agent's performance and learning efficiency far exceeded non-relational baselines, it was able to generalize to more complex scenes than it had experienced during training. Moreover, when we examined its learned internal representations, they reflected important structure about the problem and the agent's intentions. Our main contribution is a new approach for representing and reasoning about states in model-free deep reinforcement learning agents via relational inductive biases, which can offer advantages more often associated with model-based methods, such as efficiency, generalization, and interpretability, and which can scale up to meet some of the most challenging reinforcement learning environments.

Relaxed Quantization for Discretized Neural Networks    

tl;dr We introduce a technique that allows for gradient based training of quantized neural networks.

Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks that can be effectively discretized without loss of performance, we introduce a differentiable quantization procedure. Differentiability can be achieved by transforming continuous distributions over the weights and activations of the network to categorical distributions over the quantization grid. These are subsequently relaxed to continuous surrogates that can allow for efficient gradient-based optimization. We further show that stochastic rounding can be seen as a special case of the proposed approach and that under this formulation the quantization grid itself can also be optimized with gradient descent. We experimentally validate the performance of our method on MNIST, CIFAR 10 and Imagenet classification.

High Resolution and Fast Face Completion via Progressively Attentive GANs    

No tl;dr =[

Face completion is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of "holes" and the controllable attributes of filled-in fragments. Our system addresses the challenges by learning a fully end-to-end framework that trains generative adversarial networks (GANs) progressively from low resolution to high resolution with conditional vectors encoding controllable attributes. We design a novel coarse-to-fine attentive module network architecture. Our model is encouraged to attend on finer details while the network is growing to a higher resolution, thus being capable of showing progressive attention to different frequency components in a coarse-to-fine way. We term the module Frequency-oriented Attentive Module (FAM). Our system can complete faces with large structural and appearance variations using a single feed-forward pass of computation with mean inference time of 0.54 seconds for images at 1024x1024 resolution. A pilot human study shows our approach outperforms state-of-the-art face completion methods. The code will be released upon publication.

Shaping representations through communication    

tl;dr Motivated by theories of language and communication, we introduce community-based autoencoders, in which multiple encoders and decoders collectively learn structured and reusable representations.

Good representations facilitate transfer learning and few-shot learning. Motivated by theories of language and communication that explain why communities with large number of speakers have, on average, simpler languages with more regularity, we cast the representation learning problem in terms of learning to communicate. Our starting point sees traditional autoencoders as a single encoder with a fixed decoder partner that must learn to communicate. Generalizing from there, we introduce community-based autoencoders in which multiple encoders and decoders collectively learn representations by being randomly paired up on successive training iterations. Our experiments show that increasing community sizes reduce idiosyncrasies in the learned codes, resulting in more invariant representations with increased reusability and structure.

Quantization for Rapid Deployment of Deep Neural Networks    

No tl;dr =[

This paper aims at rapid deployment of the state-of-the-art deep neural networks (DNNs) to energy efficient accelerators without time-consuming fine tuning or the availability of the full datasets. Converting DNNs in full precision to limited precision is essential in taking advantage of the accelerators with reduced memory footprint and computation power. However, such a task is not trivial since it often requires the full training and validation datasets for profiling the network statistics and fine tuning the networks to recover the accuracy lost after quantization. To address these issues, we propose a simple method recognizing channel-level distribution to reduce the quantization-induced accuracy loss and minimize the required image samples for profiling. We evaluated our method on eleven networks trained on the ImageNet classification benchmark and a network trained on the Pascal VOC object detection benchmark. The results prove that the networks can be quantized into 8-bit integer precision without fine tuning.

Stochastic Adversarial Video Prediction    

No tl;dr =[

Being able to predict what may happen in the future requires an in-depth understanding of the physical and causal rules that govern the world. A model that is able to do so has a number of appealing applications, from robotic planning to representation learning. However, learning to predict raw future observations, such as frames in a video, is exceedingly challenging—the ambiguous nature of the problem can cause a naively designed model to average together possible futures into a single, blurry prediction. Recently, this has been addressed by two distinct approaches: (a) latent variational variable models that explicitly model underlying stochasticity and (b) adversarially-trained models that aim to produce naturalistic images. However, a standard latent variable model can struggle to produce realistic results, and a standard adversarially-trained model underutilizes latent variables and fails to produce diverse predictions. We show that these distinct methods are in fact complementary. Combining the two produces predictions that look more realistic to human raters and better cover the range of possible futures. Our method outperforms prior works in these aspects.

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels    

tl;dr Starting from tomorrow, never worry about your DNNs memorizing noisy labels---forget bad labels by Pumpout in an active manner!

It is challenging to train deep neural networks robustly on the industrial-level data, since labels of such data are heavily noisy, and their label generation processes are normally agnostic. To handle these issues, by using the memorization effects of deep neural networks, we may train deep neural networks on the whole dataset only the first few iterations. Then, we may employ early stopping or the small-loss trick to train them on selected instances. However, in such training procedures, deep neural networks inevitably memorize some noisy labels, which will degrade their generalization. In this paper, we propose a meta algorithm called Pumpout to overcome the problem of memorizing noisy labels. By using scaled stochastic gradient ascent, Pumpout actively squeezes out the negative effects of noisy labels from the training model, instead of passively forgetting these effects. We leverage Pumpout to upgrade two representative methods: MentorNet and Backward Correction. Empirical results on benchmark datasets demonstrate that Pumpout can significantly improve the robustness of representative methods.

On the Turing Completeness of Modern Neural Network Architectures    

tl;dr We show that the Transformer architecture and the Neural GPU are Turing complete.

Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences. In spite of their relevance, the computational properties of these alternatives have not yet been fully explored. We study the computational power of two of the most paradigmatic architectures exemplifying these mechanisms: the Transformer (Vaswani et al., 2017) and the Neural GPU (Kaiser & Sutskever, 2016). We show both models to be Turing complete exclusively based on their capacity to compute and access internal dense representations of the data. In particular, neither the Transformer nor the Neural GPU requires access to an external memory to become Turing complete. Our study also reveals some minimal sets of elements needed to obtain these completeness results.

Improving the Differentiable Neural Computer Through Memory Masking, De-allocation, and Link Distribution Sharpness Control    

No tl;dr =[

The Differentiable Neural Computer (DNC) can learn algorithmic and question answering tasks. An analysis of its internal activation patterns reveals three problems: Most importantly, content based look-up results in flat and noisy address distributions, because the lack of key-value separation makes the DNC unable to ignore memory content which is not present in the key and need to be retrieved. Second, DNC's de-allocation of memory results in aliasing, which is a problem for content-based look-up. Thirdly, chaining memory reads with the temporal linkage matrix exponentially degrades the quality of the address distribution. Our proposed fixes of these problems yield improved performance on arithmetic tasks, and also improve the mean error rate on the bAbI question answering dataset by 43%.

Evaluating Robustness of Neural Networks with Mixed Integer Programming    

tl;dr We efficiently verify the robustness of deep neural models with over 100,000 ReLUs, certifying more samples than the state-of-the-art and finding more adversarial examples than a strong first-order attack.

Neural networks trained only to optimize for training accuracy can often be fooled by adversarial examples --- slightly perturbed inputs misclassified with high confidence. Verification of networks enables us to gauge their vulnerability to such adversarial examples. We formulate verification of piecewise-linear neural networks as a mixed integer program. On a representative task of finding minimum adversarial distortions, our verifier is two to three orders of magnitude quicker than the state-of-the-art. We achieve this computational speedup via tight formulations for non-linearities, as well as a novel presolve algorithm that makes full use of all information available. The computational speedup allows us to verify properties on convolutional and residual networks with over 100,000 ReLUs --- several orders of magnitude more than networks previously verified by any complete verifier. In particular, we determine for the first time the exact adversarial accuracy of an MNIST classifier to perturbations with bounded l-∞ norm ε=0.1: for this classifier, we find an adversarial example for 4.38% of samples, and a certificate of robustness to norm-bounded perturbations for the remainder. Across all robust training procedures and network architectures considered, and for both the MNIST and CIFAR-10 datasets, we are able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack.

Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning    

No tl;dr =[

Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. However, ES can be considered a gradient-based algorithm because it performs stochastic gradient descent via an operation similar to a finite-difference approximation of the gradient. That raises the question of whether non-gradient-based evolutionary algorithms can work at DNN scales. Here we demonstrate they can: we evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari and humanoid locomotion. The Deep GA successfully evolves networks with over four million free parameters, the largest neural networks ever evolved with a traditional evolutionary algorithm. These results (1) expand our sense of the scale at which GAs can operate, (2) suggest intriguingly that in some cases following the gradient is not the best choice for optimizing performance, and (3) make immediately available the multitude of neuroevolution techniques that improve performance. We demonstrate the latter by showing that combining DNNs with novelty search, which encourages exploration on tasks with deceptive or sparse reward functions, can solve a high-dimensional problem on which reward-maximizing algorithms (e.g.\ DQN, A3C, ES, and the GA) fail. Additionally, the Deep GA is faster than ES, A3C, and DQN (it can train Atari in {\raise.17ex\hbox{$\scriptstyle\sim$}}4 hours on one workstation or {\raise.17ex\hbox{$\scriptstyle\sim$}}1 hour distributed on 720 cores), and enables a state-of-the-art, up to 10,000-fold compact encoding technique.

Multi-Agent Dual Learning    

No tl;dr =[

Dual learning has attracted much attention in machine learning, computer vision and natural language processing communities. The core idea of dual learning is to leverage the duality between the primal task (mapping from domain X to domain Y) and dual task (mapping from domain Y to X) to boost the performances of both tasks. Existing dual learning framework forms a system with two agents (one primal model and one dual model) to utilize such duality. In this paper, we extend this framework by introducing more primal and dual models, and propose the multi-agent dual learning framework. Experiments on neural machine translation and image translation tasks demonstrate the effectiveness of the new framework. In particular, our framework achieves state-of-the-art performance on IWSLT 2014 German-to-English translation with a 35.44 BLEU score and achieves a 30.67 BLEU score on WMT 2014 English-to-German translation, with over 2.2 BLEU improvement over the strong Transformer baseline.

Complement Objective Training    

tl;dr We propose Complement Objective Training (COT), a new training paradigm that optimizes both the primary and complement objectives for effectively learning the parameters of neural networks.

Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the ground-truth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to adversarial attacks.

Sequence Modelling with Memory-Augmented Recurrent Neural Networks    

tl;dr We propose a light-weight Memory-Augmented RNN (MARNN) for sequence modelling.

Processing sequential data with long term dependencies is a major challenge in many deep learning applications. In this paper, we introduce a novel architecture, the Memory-Augmented RNN (MARNN) to address this issue. The MARNN explicitly stores previous hidden states and makes use of them by an efficient memory addressing mechanism at every time-step. Compared to existing memory networks, the MARNN is more light-weight and allows direct backpropagation from output to memory. Our network can be trained on small slices of long sequential data, and thus, can theoretically boost training speed. We test the MARNN on two typical sequential modelling tasks. We achieve a competitive 1.202 Bits- per-character on the Penn Treebank character-level language modelling task, and achieve state-of-the-art performance of recall at high tIoUs on the THUMOS’ 14 temporal action detection and proposal task.

Mode Normalization    

tl;dr We present a novel normalization method for deep neural networks that is robust to multi-modalities in intermediate feature distributions.

Normalization methods are a central building block in the deep learning toolbox. They accelerate and stabilize training, while decreasing the dependence on manually tuned learning rate schedules. When learning from multi-modal distributions, the effectiveness of batch normalization (BN), arguably the most prominent normalization method, is reduced. As a remedy, we propose a more flexible approach: by extending the normalization to more than a single mean and variance, we detect modes of data on-the-fly, jointly normalizing samples that share common features. We demonstrate that our method outperforms BN and other widely used normalization techniques in several experiments, including single and multi-task datasets.

DynCNN: An Effective Dynamic Architecture on Convolutional Neural Network for Surveillance Videos    

tl;dr An optimizing architecture on CNN for surveillance videos with 75.7% reduction on FLOPs and 2.2 times improvement on FPS

The large-scale surveillance video analysis becomes important as the development of intelligent city. The heavy computation resources neccessary for state-of-the-art deep learning model makes the real-time processing hard to be implemented. This paper exploits the characteristic of high scene similarity generally existing in surveillance videos and proposes dynamic convolution reusing the previous feature map to reduce the computation amount. We tested the proposed method on 45 surveillance videos with various scenes. The experimental results show that dynamic convolution can reduce up to 75.7% of FLOPs while preserving the precision within 0.7% mAP. Furthermore, the dynamic convolution can enhance the processing time up to 2.2 times.

Unsupervised Conditional Generation using noise engineered mode matching GAN    

tl;dr A GAN model where an inversion mapping from the generated data space to an engineered latent space is learned such that properties of the data generating distribution are matched to those of the latent distribution.

Conditional generation refers to the process of sampling from an unknown distribution conditioned on semantics of the data. This can be achieved by augmenting the generative model with the desired semantic labels, albeit it is not straightforward in an unsupervised setting where the semantic label of every data sample is unknown. In this paper, we address this issue by proposing a method that can generate samples conditioned on the properties of a latent distribution engineered in accordance with a certain data prior. In particular, a latent space inversion network is trained in tandem with a generative adversarial network such that the modal properties of the latent space distribution are induced in the data generating distribution. We demonstrate that our model despite being fully unsupervised, is effective in learning meaningful representations through its mode matching property. We validate our method on multiple unsupervised tasks such as conditional generation, dataset attribute discovery and inference using three real world image datasets namely MNIST, CIFAR-10 and CELEB-A and show that the results are comparable to the state-of-the-art methods.

Trellis Networks for Sequence Modeling    

tl;dr Trellis networks are a new sequence modeling architecture that bridges recurrent and convolutional models and sets a new state of the art on word- and character-level language modeling.

We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers. On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices. Thus trellis networks with general weight matrices generalize truncated recurrent networks. We leverage these connections to design high-performing trellis networks that absorb structural and algorithmic elements from both recurrent and convolutional models. Experiments demonstrate that trellis networks outperform the current state of the art on a variety of challenging benchmarks, including word-level language modeling on Penn Treebank and WikiText-103, character-level language modeling on Penn Treebank, and stress tests designed to evaluate long-term memory retention.

Learning space time dynamics with PDE guided neural networks    

No tl;dr =[

Spatio-Temporal processes bear a central importance in many applied scientific fields. Generally, differential equations are used to describe these processes. In this work, we address the problem of learning spatio-temporal dynamics with neural networks when only partial information on the system's state is available. Taking inspiration from the dynamical system approach, we outline a general framework in which complex dynamics generated by families of differential equations can be learned in a principled way. Two models are derived from this framework. We demonstrate how they can be applied in practice by considering the problem of forecasting fluid flows. We show how the underlying equations fit into our formalism and evaluate our method by comparing with standard baselines.

Bridging HMMs and RNNs through Architectural Transformations    

tl;dr Are HMMs a special case of RNNs? We investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization and provide new insights.

A distinct commonality between HMMs and RNNs is that they both learn hidden representations for sequential data. In addition, it has been noted that the backward computation of the Baum-Welch algorithm for HMMs is a special case of the back propagation algorithm used for neural networks (Eisner (2016)). Do these observations suggest that, despite their many apparent differences, HMMs are a special case of RNNs? In this paper, we investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization, to answer this question. In particular, we investigate three key design factors—independence assumptions between the hidden states and the observation, the placement of softmax, and the use of non-linearity—in order to pin down their empirical effects. We present a comprehensive empirical study to provide insights on the interplay between expressivity and interpretability with respect to language modeling and parts-of-speech induction.

Integral Pruning on Activations and Weights for Efficient Neural Networks    

tl;dr This work advances DNN compression beyond the weights to the activations by integrating the activation pruning with the weight pruning.

With the rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for efficient deployment. This work aims to advance the compression beyond the weights to the activations of DNNs. We propose the Integral Pruning (IP) technique which integrates the activation pruning with the weight pruning. Through the learning on the different importance of neuron responses and connections, the generated network, namely IPnet, balances the sparsity between activations and weights and therefore further improves execution efficiency. The feasibility and effectiveness of IPnet are thoroughly evaluated through various network models with different activation functions and on different datasets. With <0.5% disturbance on the testing accuracy, IPnet saves 71.1% ~ 96.35% of computation cost, compared to the original dense models with up to 5.8x and 10x reductions in activation and weight numbers, respectively.

A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks    

No tl;dr =[

Depending on how much information an adversary can access to, adversarial attacks can be classified as white-box attack and black-box attack. In both cases, optimization-based attack algorithms can achieve relatively low distortions and high attack success rates. However, they usually suffer from poor time and query complexities, thereby limiting their practical usefulness. In this work, we focus on the problem of developing efficient and effective optimization-based adversarial attack algorithms. In particular, we propose a novel adversarial attack framework for both white-box and black-box settings based on the non-convex Frank-Wolfe algorithm. We show in theory that the proposed attack algorithms are efficient with an O(1/\sqrt{T}) convergence rate, which, to our knowledge, is the first convergence rate analysis for the zeroth-order non-convex Frank-Wolfe type algorithm. The empirical results on attacking Inception V3 model with the ImageNet dataset also verify the efficiency and effectiveness of the proposed algorithms. They attain a 100% attack success rate in both white-box and black-box attacks, and are more time and query efficient than the state-of-the-art baseline algorithms.

Scalable Unbalanced Optimal Transport using Generative Adversarial Networks    

tl;dr We propose new methodology for unbalanced optimal transport using generative adversarial networks.

Generative adversarial networks (GANs) are an expressive class of neural generative models with tremendous success in modeling high-dimensional continuous measures. In this paper, we present a scalable method for unbalanced optimal transport (OT) based on the generative-adversarial framework. We formulate unbalanced OT as a problem of simultaneously learning a transport map and a scaling factor that push a source measure to a target measure in a cost-optimal manner, and propose a new algorithm based on stochastic alternating gradient updates, similar in practice to GANs. We also provide theoretical justification for this formulation, showing that it is closely related to an existing static formulation by Liero et al. (2018), and perform numerical experiments demonstrating how this methodology could be applied to population modeling.


tl;dr This is a work aiming for boosting all the existing pruning and mimic method.

In this paper, we propose an efficient framework to accelerate convolutional neural networks. We utilize two types of acceleration methods: pruning and hints. Pruning can reduce model size by removing channels of layers. Hints can improve the performance of student model by transferring knowledge from teacher model. We demonstrate that pruning and hints are complementary to each other. On one hand, hints can benefit pruning by maintaining similar feature representations. On the other hand, the model pruned from teacher networks is a good initialization for student model, which increases the transferability between two networks. Our approach performs pruning stage and hints stage iteratively to further improve the performance. Furthermore, we propose an algorithm to reconstruct the parameters of hints layer and make the pruned model more suitable for hints. Experiments were conducted on various tasks including classification and pose estimation. Results on CIFAR-10, ImageNet and COCO demonstrate the generalization and superiority of our framework.

Temporal Gaussian Mixture Layer for Videos    

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We introduce a new convolutional layer named the Temporal Gaussian Mixture (TGM) layer and present how it can be used to efficiently capture longer-term temporal information in continuous activity videos. The TGM layer is a temporal convolutional layer governed by a much smaller set of parameters (e.g., location/variance of Gaussians) that are fully differentiable. We present our fully convolutional video models with multiple TGM layers for activity detection. The experiments on multiple datasets including Charades and MultiTHUMOS confirm the effectiveness of TGM layers, outperforming the state-of-the-arts.

NICE: noise injection and clamping estimation for neural network quantization    

tl;dr Combine noise injection, gradual quantization and activation clamping learning to achieve state-of-the-art 3,4 and 5 bit quantization

Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning leads to groundbreaking performance in these domains, the networks used are very demanding computationally and are far from real-time even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error. The NICE method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve the accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with low as 3-bit weights and 3 -bit activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low power real-time applications.

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design    

tl;dr Improved training of current flow-based generative models (Glow and RealNVP) on density estimation benchmarks

Flow-based generative models are powerful exact likelihood models with the benefit of efficient sampling and inference. Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we carefully investigate three design choices employed by prior flow-based models that turn out to be limiting: (1) uniform noise is a sub-optimal dequantization choice that hurts both training loss and generalization; (2) commonly used affine coupling flows are not expressive enough; (3) conv-net based parametrization of flows fails to capture the global image context. Based on our findings, we propose Flow++, a set of alternative design choices that significantly improve the density modeling capacity of flow-based models.

Transferring Knowledge across Learning Processes    

tl;dr We propose Leap, a framework that transfers knowledge across learning processes by minimizing the expected distance the training process travels on a task's loss surface.

In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at a higher level of abstraction is needed. We propose Leap, a framework that achieves this by transferring knowledge across learning processes. We associate each task with a manifold on which the training process travels from initialization to final parameters and construct a meta learning objective that minimizes the expected length of this path. Our framework leverages only information obtained during training and can be computed on the fly at negligible cost. We demonstrate that our framework outperforms competing methods, both in meta learning and transfer learning, on a set of computer vision tasks. Finally, we demonstrate that Leap can transfer knowledge across learning processes in demanding Reinforcement learning environments (Atari) that involve millions of gradient steps.

MixFeat: Mix Feature in Latent Space Learns Discriminative Space    

tl;dr We provide a novel method named MixFeat, which directly makes the latent space discriminative.

Deep learning methods perform well in various tasks. However, the over-fitting problem remains, where the performance decreases for unknown data. We here provide a novel method named MixFeat, which directly makes the latent space discriminative. MixFeat mixes two feature maps in each latent space and uses one of their labels for learning. We report improved results obtained using existing network models with MixFeat on CIFAR-10/100 datasets. In addition, we show that MixFeat effectively reduces the over-fitting problem even in the case that the training dataset is small or contains errors. We argue that MixFeat is complementary with existing methods that mix both images and labels, in that MixFeat is suitable for discrimination tasks while existing methods are suitable for regression tasks. MixFeat is easy to implement and can be added to various network models without additional computational cost in the inference phase.

Outlier Detection from Image Data    

tl;dr A novel approach that detects outliers from image data, while at the same time preserving the classification accuracy of the multi-class classification problem

Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data. In this work we propose a novel image outlier detection approach (IOD for short) that leverages the cutting-edge image classifier to discover outliers without using any labeled outlier. We observe that although intuitively the confidence that a convolutional neural network (CNN) has that an image belongs to a particular class could serve as outlierness measure to each image, directly applying this confidence to detect outlier does not work well. This is because CNN often has high confidence on an outlier image that does not belong to any target class due to its generalization ability that ensures the high accuracy in classification. To solve this issue, we propose a Deep Neural Forest-based approach that harmonizes the contradictory requirements of accurately classifying images and correctly detecting the outlier images. Our experiments using several benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN demonstrate the effectiveness of our IOD approach for outlier detection, capturing more than 90% of outliers generated by injecting one image dataset into another, while still preserving the classification accuracy of the multi-class classification problem.

Visualizing and Understanding Generative Adversarial Networks    

tl;dr GAN representations are examined in detail, and sets of representation units are found that control the generation of semantic concepts in the output.

Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications. As an active research topic, many GAN variants have emerged with immprovements in sample quality and training stability. However, visualization and understanding of GANs is largely missing. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to \concepts with a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered \concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing ``artifacts'' units, to interactively manipulating objects in the scene. We will open source our interactive online tools to help researchers and practitioners better understand their models.

Optimal margin Distribution Network    

tl;dr This paper presents a deep neural network embedding a loss function in regard to the optimal margin distribution, which alleviates the overfitting problem theoretically and empirically.

Recent research about margin theory has proved that maximizing the minimum margin like support vector machines does not necessarily lead to better performance, and instead, it is crucial to optimize the margin distribution. In the meantime, margin theory has been used to explain the empirical success of deep network in recent studies. In this paper, we present ODN (the Optimal margin Distribution Network), a network which embeds a loss function in regard to the optimal margin distribution. We give a theoretical analysis for our method using the PAC-Bayesian framework, which confirms the significance of the margin distribution for classification within the framework of deep networks. In addition, empirical results show that the ODN model always outperforms the baseline cross-entropy loss model consistently across different regularization situations. And our ODN model also outperforms the other three loss models in generalization task through limited training data.

Improving MMD-GAN Training with Repulsive Loss Function    

tl;dr Rearranging the terms in maximum mean discrepancy yields a much better loss function for generative adversarial nets

Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance heavily depends on the loss functions used in training. This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions. First, we argue that the existing MMD loss function may discourage the learning of data structures as it attempts to attract the discriminator outputs of real data. To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD. Second, inspired by the hinge loss, we propose a bounded Gaussian kernel to stabilize the training of MMD-GAN. The proposed methods are applied to the unsupervised image generation tasks on CIFAR-10, STL-10, CelebA, and LSUN bedroom datasets. Results show that the repulsive loss function significantly improves over the MMD loss at no additional computational cost and outperforms other representative loss functions. The proposed methods achieved an FID score of 16.21 on the CIFAR-10 dataset using a single DCGAN network and spectral normalization.


tl;dr We propose new model that can disentangle multiple dynamic factors in sequential data

A state-of-the-art generative model, a ”factorized action variational autoencoder (FAVAE),” is presented for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision. The purpose of disentangled representation learning is to obtain interpretable and transferable representations from data. We focused on the disentangled representation of sequential data because there is a wide range of potential applications if disentanglement representation is extended to sequential data such as video, speech, and stock price data. Sequential data is characterized by dynamic factors and static factors: dynamic factors are time-dependent, and static factors are independent of time. Previous works succeed in disentangling static factors and dynamic factors by explicitly modeling the priors of latent variables to distinguish between static and dynamic factors. However, this model can not disentangle representations between dynamic factors, such as disentangling ”picking” and ”throwing” in robotic tasks. In this paper, we propose new model that can disentangle multiple dynamic factors. Since our method does not require modeling priors, it is capable of disentangling ”between” dynamic factors. In experiments, we show that FAVAE can extract the disentangled dynamic factors.

Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks    

tl;dr We adapt bilateral filtering as a layer in a neural network which improves robustness to adversarial examples using nonlocal filtering.

Recent analysis of deep neural networks has revealed their vulnerability to carefully structured adversarial examples. Many effective algorithms exist to craft these adversarial examples, but performant defenses seem to be far away. In this work, we explore the use of edge-aware bilateral filtering as a projection back to the space of natural images. We show that bilateral filtering is an effective defense in multiple attack settings, where the strength of the adversary gradually increases. In the case of adversary who has no knowledge of the defense, bilateral filtering can remove more than 90% of adversarial examples from a variety of different attacks. To evaluate against an adversary with complete knowledge of our defense, we adapt the bilateral filter as a trainable layer in a neural network and show that adding this layer makes ImageNet images significantly more robust to attacks. When trained under a framework of adversarial training, we show that the resulting model is hard to fool with even the best attack methods.

Generative Adversarial Networks for Extreme Learned Image Compression    

tl;dr GAN-based extreme image compression method using less than half the bits of the SOTA engineered codec while preserving visual quality

We propose a framework for extreme learned image compression based on Generative Adversarial Networks (GANs), obtaining visually pleasing images at significantly lower bitrates than previous methods. This is made possible through our GAN formulation of learned compression combined with a generator/decoder which operates on the full-resolution image and is trained in combination with a multi-scale discriminator. Additionally, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, therefore only requiring the storage of the preserved region and the semantic label map. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.

Meta Learning with Fast/Slow Learners    

tl;dr We applied multiple meta-strategy to improve meta-learning performance on base CNNs.

Meta-learning has recently achieved success in many optimization problems. In general, a meta learner g(.) could be learned for a base model f(.) on a variety of tasks, such that it can be more efficient on a new task. In this paper, we make some key modifications to enhance the performance of meta-learning models. (1) we leverage different meta-strategies for different modules to optimize them separately: we use conservative “slow learners” on low-level basic feature representation layers and “fast learners” on high-level task-specific layers; (2) Furthermore, we provide theoretical analysis on why the proposed approach works, based on a case study on a two-layer MLP. We evaluate our model on synthetic MLP regression, as well as low-shot learning tasks on Omniglot and ImageNet benchmarks. We demonstrate that our approach is able to achieve state-of-the-art performance.

Stable Recurrent Models    

tl;dr Stable recurrent models can be approximated by feed-forward networks and empirically perform as well as unstable models on benchmark tasks.

Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks. In this work, we conduct a thorough investigation of stable recurrent models. Theoretically, we prove stable recurrent neural networks are well approximated by feed-forward networks for the purpose of both inference and training by gradient descent. Empirically, we demonstrate stable recurrent models often perform as well as their unstable counterparts on benchmark sequence tasks. Taken together, these findings shed light on the effective power of recurrent networks and suggest much of sequence learning happens, or can be made to happen, in the stable regime. Moreover, our results help to explain why in many cases practitioners succeed in replacing recurrent models by feed-forward models.

N-Ary Quantization for CNN Model Compression and Inference Acceleration    

tl;dr We propose a quantization scheme for weights and activations of deep neural networks. This reduces the memory footprint substantially and accelerates inference.

The tremendous memory and computational complexity of Convolutional Neural Networks (CNNs) prevents the inference deployment on resource-constrained systems. As a result, recent research focused on CNN optimization techniques, in particular quantization, which allows weights and activations of layers to be represented with just a few bits while achieving impressive prediction performance. However, aggressive quantization techniques still fail to achieve full-precision prediction performance on state-of-the-art CNN architectures on large-scale classification tasks. In this work we propose a method for weight and activation quantization that is scalable in terms of quantization levels (n-ary representations) and easy to compute while maintaining the performance close to full-precision CNNs. Our weight quantization scheme is based on trainable scaling factors and a nested-means clustering strategy which is robust to weight updates and therefore exhibits good convergence properties. The flexibility of nested-means clustering enables exploration of various n-ary weight representations with the potential of high parameter compression. For activations, we propose a linear quantization strategy that takes the statistical properties of batch normalization into account. We demonstrate the effectiveness of our approach using state-of-the-art models on ImageNet.

Generalized Capsule Networks with Trainable Routing Procedure    

tl;dr A scalable capsule network

CapsNet (Capsule Network) was first proposed by Sabour et al. (2017) and lateranother version of CapsNet was proposed by Hinton et al. (2018). CapsNet hasbeen proved effective in modeling spatial features with much fewer parameters.However, the routing procedures (dynamic routing and EM routing) in both pa-pers are not well incorporated into the whole training process, and the optimalnumber for the routing procedure has to be found manually. We propose Gen-eralized GapsNet (G-CapsNet) to overcome this disadvantages by incorporatingthe routing procedure into the optimization. We implement two versions of G-CapsNet (fully-connected and convolutional) on CAFFE (Jia et al. (2014)) andevaluate them by testing the accuracy on MNIST & CIFAR10, the robustness towhite-box & black-box attack, and the generalization ability on GAN-generatedsynthetic images. We also explore the scalability of G-CapsNet by constructinga relatively deep G-CapsNet. The experiment shows that G-CapsNet has goodgeneralization ability and scalability.

Graph Learning Network: A Structure Learning Algorithm    

tl;dr Methods for simultaneous prediction of nodes' feature embeddings and adjacency matrix, and how to learn this process.

Graph prediction methods that work closely with the structure of the data, e.g., graph generation, commonly ignore the content of its nodes. On the other hand, the solutions that consider the node’s information, e.g., classification, ignore the structure of the whole. And some methods exist in between, e.g., link prediction, but predict the structure piece-wise instead of considering the graph as a whole. We hypothesize that by jointly predicting the structure of the graph and its nodes’ features, we can improve both tasks. We propose the Graph Learning Network (GLN), a simple yet effective process to learn node embeddings and structure prediction functions. Our model uses graph convolutions to propose expected node features, and predict the best structure based on them. We repeat these steps sequentially to enhance the prediction and the embeddings. In contrast to existing generation methods that rely only on the structure of the data, we use the feature on the nodes to predict better relations, similar to what link prediction methods do. However, we propose an holistic approach to process the whole graph for our predictions. Our experiments show that our method predicts consistent structures across a set of problems, while creating meaningful node embeddings.

Statistical Characterization of Deep Neural Networks and their Sensitivity    

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Despite their ubiquity, it remains an active area of research to fully understand deep neural networks (DNNs) and the reasons of their empirical success. We contribute to this effort by introducing a principled approach to statistically characterize DNNs and their sensitivity. By distinguishing between randomness from input data and from model parameters, we study how central and non-central moments of network activation and sensitivity evolve during propagation. Thereby, we provide novel statistical insights on the hypothesis space of input-output mappings encoded by different architectures. Our approach applies both to fully-connected and convolutional networks and incorporates most ingredients of modern DNNs: rectified linear unit (ReLU) activation, batch normalization, skip connections.

Generalizable Adversarial Training via Spectral Normalization    

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Deep neural networks (DNNs) have set benchmarks on a wide array of supervised learning tasks. Trained DNNs, however, often lack robustness to minor adversarial perturbations to the input, which undermines their true practicality. Recent works have increased the robustness of DNNs by fitting networks using adversarially-perturbed training samples, but the improved performance can still be far below the performance seen in non-adversarial settings. A significant portion of this gap can be attributed to the decrease in generalization performance due to adversarial training. In this work, we extend the notion of margin loss to adversarial settings and bound the generalization error for DNNs trained under several well-known gradient-based attack schemes, motivating an effective regularization scheme based on spectral normalization of the DNN's weight matrices. We also provide a computationally-efficient method for normalizing the spectral norm of convolutional layers with arbitrary stride and padding schemes in deep convolutional networks. We evaluate the power of spectral normalization extensively on combinations of datasets, network architectures, and adversarial training schemes.

Adversarially Robust Training through Structured Gradient Regularization    

tl;dr We propose a novel data-dependent structured gradient regularizer to increase the robustness of neural networks against adversarial perturbations.

We propose a novel data-dependent structured gradient regularizer to increase the robustness of neural networks vis-a-vis adversarial perturbations. Our regularizer can be derived as a controlled approximation from first principles, leveraging the fundamental link between training with noise and regularization. It adds very little computational overhead during learning and is simple to implement generically in standard deep learning frameworks. Our experiments provide strong evidence that structured gradient regularization can act as an effective first line of defense against attacks based on long-range correlated signal corruptions.

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow    

tl;dr Regularizing adversarial learning with an information bottleneck, applied to imitation learning, inverse reinforcement learning, and generative adversarial networks.

Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the performance of the generator and discriminator is critical, since a discriminator that achieves very high accuracy will produce relatively uninformative gradients. In this work, we propose a simple and general technique to constrain information flow in the discriminator by means of an information bottleneck. By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients. We demonstrate that our proposed variational discriminator bottleneck (VDB) leads to significant improvements across three distinct application areas for adversarial learning algorithms. Our primary evaluation studies the applicability of the VDB to imitation learning of dynamic continuous control skills, such as running. We show that our method can learn such skills directly from raw video demonstrations, substantially outperforming prior adversarial imitation learning methods. The VDB can also be combined with adversarial inverse reinforcement learning to learn parsimonious reward functions that can be transferred and re-optimized in new settings. Finally, we demonstrate that VDB can train GANs more effectively for image generation, improving upon a number of prior stabilization methods.

S-System, Geometry, Learning, and Optimization: A Theory of Neural Networks    

tl;dr We present a formal measure-theoretical theory of neural networks (NN) that quantitatively shows NNs renormalize on semantic difference, and under practical conditions large size deep nonlinear NNs can optimize objective functions to zero losses.

We present a formal measure-theoretical theory of neural networks (NN) built on {\it probability coupling theory}. Particularly, we present an algorithm framework, Hierarchical Measure Group and Approximate System (HMGAS), nicknamed S-System, of which NNs are special cases. In addition to many other results, the framework enables us to prove that 1) NNs implement {\it renormalization group (RG)} using information geometry, which points out that the large scale property to renormalize is dual Bregman divergence and completes the analog between NNs and RG; 2) and under a set of {\it realistic} boundedness and diversity conditions, for {\it large size nonlinear deep} NNs with a class of losses, including the hinge loss, all local minima are global minima with zero loss errors, using random matrix theory.

Meta-learning with differentiable closed-form solvers    

tl;dr We propose a simple meta-learning algorithm capable of adapting base learners such as ridge or logistic regression efficiently, by backpropagating through their closed-form solutions. We show strong performance on three few-shot learning benchmarks.

Adapting deep networks to new concepts from few examples is challenging, due to the high computational and data requirements of standard fine-tuning procedures. Most work on few-shot learning has thus focused on simple learning techniques for adaptation, such as nearest neighbours or gradient descent. Nonetheless, the machine learning literature contains a wealth of methods that learn non-deep models very efficiently. In this work we propose to use these fast convergent methods as the main adaptation mechanism for few-shot learning. The main idea is to teach a deep network to use standard machine learning tools, such as logistic regression, as part of its own internal model, enabling it to quickly adapt to novel tasks. This requires back-propagating errors through the solver steps. While normally the cost of the matrix operations involved in such process would be significant, by using the Woodbury identity we can make the small number of examples work to our advantage. We propose both closed-form and iterative solvers, based on ridge regression and logistic regression components. Our methods constitute a simple and novel approach to the problem of few-shot learning and achieve performance competitive with or superior to the state of the art on three benchmarks.

An Energy-Based Framework for Arbitrary Label Noise Correction    

tl;dr We show how to learn a discriminative representation using an energy based semi-supervised model and we show how to use it to correct input dependent label noise of various types on several datasets.

We propose an energy-based framework for correcting mislabelled training examples in the context of binary classification. While existing work addresses random and class-dependent label noise, we focus on feature dependent label noise, which is ubiquitous in real-world data and difficult to model. Two elements distinguish our approach from others: 1) instead of relying on the original feature space, we employ an autoencoder to learn a discriminative representation and 2) we introduce an energy-based formalism for the label correction problem. We prove that a discriminative representation can be learned by training a generative model using a loss function comprised of the difference of energies corresponding to each class. The learned energy value for each training instance is compared to the original training labels and contradictions between energy assignment and training label are used to correct labels. We validate our method across eight datasets, spanning synthetic and realistic settings, and demonstrate the technique's state-of-the-art label correction performance. Furthermore, we derive analytical expressions to show the effect of label noise on the gradients of empirical risk.

Universal Transformers    

tl;dr We introduce the Universal Transformer, a self-attentive parallel-in-time recurrent sequence model that outperforms Transformers and LSTMs on a wide range of sequence-to-sequence tasks, including machine translation.

Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset.

Unseen Action Recognition with Multimodal Learning    

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In this paper, we present a method to learn a joint multimodal representation space that allows for the recognition of unseen activities in videos. We compare the effect of placing various constraints on the embedding space using paired text and video data. Additionally, we propose a method to improve the joint embedding space using an adversarial formulation with unpaired text and video data. In addition to testing on publicly available datasets, we introduce a new, large-scale text/video dataset. We experimentally confirm that learning such shared embedding space benefits three difficult tasks (i) zero-shot activity classification, (ii) unsupervised activity discovery, and (iii) unseen activity captioning.

Weakly-supervised Knowledge Graph Alignment with Adversarial Learning    

tl;dr This paper studies weakly-supervised knowledge graph alignment with adversarial training frameworks.

Aligning knowledge graphs from different sources or languages, which aims to align both the entity and relation, is critical to a variety of applications such as knowledge graph construction and question answering. Existing methods of knowledge graph alignment usually rely on a large number of aligned knowledge triplets to train effective models. However, these aligned triplets may not be available or are expensive to obtain for many domains. Therefore, in this paper we study how to design fully-unsupervised methods or weakly-supervised methods, i.e., to align knowledge graphs without or with only a few aligned triplets. We propose an unsupervised framework based on adversarial training, which is able to map the entities and relations in a source knowledge graph to those in a target knowledge graph. This framework can be further seamlessly integrated with existing supervised methods, where only a limited number of aligned triplets are utilized as guidance. Experiments on real-world datasets prove the effectiveness of our proposed approach in both the weakly-supervised and unsupervised settings.

The Forward-Backward Embedding of Directed Graphs    

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We introduce a novel embedding of directed graphs derived from the singular value decomposition (SVD) of the normalized adjacency matrix. Specifically, we show that, after proper normalization of the singular vectors, the distances between vectors in the embedding space are proportional to the mean commute times between the corresponding nodes by a forward-backward random walk in the graph, which follows the edges alternately in forward and backward directions. In particular, two nodes having many common successors in the graph tend to be represented by close vectors in the embedding space. More formally, we prove that our representation of the graph is equivalent to the spectral embedding of some co-citation graph, where nodes are linked with respect to their common set of successors in the original graph. The interest of our approach is that it does not require to build this co-citation graph, which is typically much denser than the original graph. Experiments on real datasets show the efficiency of the approach.

Towards the first adversarially robust neural network model on MNIST    

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Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans. We show that even the widely recognized and by far most successful L-inf defense by Madry et~al. (1) has lower L0 robustness than undefended networks and still highly susceptible to L2 perturbations, (2) classifies unrecognizable images with high certainty, (3) performs not much better than simple input binarization and (4) features adversarial perturbations that make little sense to humans. These results suggest that MNIST is far from being solved in terms of adversarial robustness. We present a novel robust classification model that performs analysis by synthesis using learned class-conditional data distributions. We derive bounds on the robustness and go to great length to empirically evaluate our model using maximally effective adversarial attacks by (a) applying decision-based, score-based, gradient-based and transfer-based attacks for several different Lp norms, (b) by designing a new attack that exploits the structure of our defended model and (c) by devising a novel decision-based attack that seeks to minimize the number of perturbed pixels (L0). The results suggest that our approach yields state-of-the-art robustness on MNIST against L0, L2 and L-inf perturbations and we demonstrate that most adversarial examples are strongly perturbed towards the perceptual boundary between the original and the adversarial class.

Discriminator Rejection Sampling    

tl;dr We use a GAN discriminator to perform an approximate rejection sampling scheme on the output of the GAN generator.

We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution. We show that under quite strict assumptions, this will allow us to recover the data distribution exactly. We then examine where those strict assumptions break down and design a practical algorithm—called Discriminator Rejection Sampling (DRS)—that can be used on real data-sets. Finally, we demonstrate the efficacy of DRS on a mixture of Gaussians and on the state of the art SAGAN model. On ImageNet, we train an improved baseline that increases the best published Inception Score from 52.52 to 62.36 and reduces the Frechet Inception Distance from 18.65 to 14.79. We then use DRS to further improve on this baseline, improving the Inception Score to 76.08 and the FID to 13.75.

Harmonic Unpaired Image-to-image Translation    

tl;dr Smooth regularization over sample graph for unpaired image-to-image translation results in significantly improved consistency

The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations. In this paper, we take a manifold view of the problem by introducing a smoothness constraint over the sample graph to attain harmonic functions to enforce consistent mappings during the translation. We develop HarmonicGAN to learn bi-directional translations between the source and the target domain. With the help of similarity-consistency, the inherent self-consistency property of samples can be maintained. Distance metrics defined on two types of features including histogram and CNN are exploited. Under an identical problem setting as CycleGAN without additional manual inputs, HarmonicGAN demonstrates a significant qualitative and quantitative improvement over the state of the art, as well as improved interpretability. We show experimental results in a number of applications including medical imaging, object transfiguration, and semantic labeling. We outperform the competing methods in all tasks, and for a medical imaging task in particular our method turns CycleGAN from a failure to a success, halving the mean-squared error, and generating images that radiologists prefer over competing methods in 95% of cases.

Multi-objective training of Generative Adversarial Networks with multiple discriminators    

tl;dr We introduce hypervolume maximization for training GANs with multiple discriminators, showing performance improvements in terms of sample quality and diversity.

Recent literature has demonstrated promising results on the training of Generative Adversarial Networks by employing a set of discriminators, as opposed to the traditional game involving one generator against a single adversary. Those methods perform single-objective optimization on some simple consolidation of the losses, e.g. an average. In this work, we revisit the multiple-discriminator approach by framing the simultaneous minimization of losses provided by different models as a multi-objective optimization problem. Specifically, we evaluate the performance of multiple gradient descent and the hypervolume maximization algorithm on a number of different datasets. Moreover, we argue that the previously proposed methods and hypervolume maximization can all be seen as variations of multiple gradient descent in which the update direction computation can be done efficiently. Our results indicate that hypervolume maximization presents a better compromise between sample quality and diversity, and computational cost than previous methods.

BlackMarks: Black-box Multi-bit Watermarking for Deep Neural Networks    

tl;dr Proposing the first watermarking framework for multi-bit signature embedding and extraction using the outputs of the DNN.

Deep Neural Networks (DNNs) are increasingly deployed in cloud servers and autonomous agents due to their superior performance. The deployed DNN is either leveraged in a white-box setting (model internals are publicly known) or a black-box setting (only model outputs are known) depending on the application. A practical concern in the rush to adopt DNNs is protecting the models against Intellectual Property (IP) infringement. We propose BlackMarks, the first end-to-end multi-bit watermarking framework that is applicable in the black-box scenario. BlackMarks takes the pre-trained unmarked model and the owner’s binary signature as inputs. The output is the corresponding marked model with specific keys that can be later used to trigger the embedded watermark. To do so, BlackMarks first designs a model-dependent encoding scheme that maps all possible classes in the task to bit ‘0’ and bit ‘1’. Given the owner’s watermark signature (a binary string), a set of key image and label pairs is designed using targeted adversarial attacks. The watermark (WM) is then encoded in the distribution of output activations of the DNN by fine-tuning the model with a WM-specific regularized loss. To extract the WM, BlackMarks queries the model with the WM key images and decodes the owner’s signature from the corresponding predictions using the designed encoding scheme. We perform a comprehensive evaluation of BlackMarks’ performance on MNIST, CIFAR-10, ImageNet datasets and corroborate its effectiveness and robustness. BlackMarks preserves the functionality of the original DNN and incurs negligible WM embedding overhead as low as 2.054%.

Exploring Curvature Noise in Large-Batch Stochastic Optimization    

tl;dr Engineer large-batch training such that we retain fast training while achieving better generalization.

Using stochastic gradient descent (SGD) with large batch-sizes to train deep neural networks is an increasingly popular technique. By doing so, one can improve parallelization by scaling to multiple workers (GPUs) and hence leading to significant reductions in training time. Unfortunately, a major drawback is the so-called generalization gap: large-batch training typically leads to a degradation in generalization performance of the model as compared to small-batch training. In this paper, we propose to correct this generalization gap by adding diagonal Fisher curvature noise to large-batch gradient updates. We provide a theoretical analysis of our method in the convex quadratic setting. Our empirical study with state-of-the-art deep learning models shows that our method not only improves the generalization performance in large-batch training but furthermore, does so in a way where the training convergence remains desirable and the training duration is not elongated. We additionally connect our method to recent works on loss surface landscape in the experimental section.

Evolutionary-Neural Hybrid Agents for Architecture Search    

tl;dr We propose a class of Evolutionary-Neural hybrid agents, that retain the best qualities of the two approaches.

Neural Architecture Search has recently shown potential to automate the design of Neural Networks. The use of Neural Network agents trained with Reinforcement Learning can offer the possibility to learn complex patterns, as well as the ability to explore a vast and compositional search space. On the other hand, evolutionary algorithms offer the greediness and sample efficiency needed for such an application, as each sample requires a considerable amount of resources. We propose a class of Evolutionary-Neural hybrid agents (Evo-NAS), that retain the best qualities of the two approaches. We show that the Evo-NAS agent can outperform both Neural and Evolutionary agents, both on a synthetic task, and on architecture search for a suite of text classification datasets.

Uncertainty in Multitask Transfer Learning    

tl;dr A scalable method for learning an expressive prior over neural networks across multiple tasks.

Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks. This results in a rich prior capable of few-shot learning on new tasks. The posterior can go beyond the mean field approximation and yields good uncertainty on the performed experiments. Analysis on toy tasks show that it can learn from significantly different tasks while finding similarities among them. Experiments on Mini-Imagenet reach state of the art with 74.5% accuracy on 5 shot learning. Finally, we provide two new benchmarks, each showing a failure mode of existing meta learning algorithms such as MAML and prototypical Networks.

Cosine similarity-based Adversarial process    

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An adversarial process between two deep neural networks is a promising approach to train robust networks. In this study, we propose a framework for training networks that eliminates subsidiary information via the adversarial process. The objective of the proposed framework is to train a primary model that is robust to existing subsidiary information. This primary model can be used for various recognition tasks, such as digit recognition and speaker identification. Subsidiary information refers to the factors that might decrease the performance of the primary model such as channel information in speaker recognition and noise information in digit recognition. Our proposed framework comprises two discriminative models for the primary and subsidiary task, as well as an encoder network for feature representation. A subsidiary task is an operation associated with subsidiary information such as identifying the noise type. The discriminative model for the subsidiary task is trained for modeling the dependency of subsidiary class labels on codes from the encoder. Therefore, we expect that subsidiary information could be eliminated by training the encoder to reduce the dependency between the class labels and codes. In order to do so, we train the weight parameters of the subsidiary model; then, we develop the codes and the parameters of subsidiary model to make them orthogonal. For this purpose, we design a loss function to train the encoder based on cosine similarity between the weight parameters of the subsidiary model and codes. Finally, the proposed framework involves repeatedly performing the adversarial process of modeling the subsidiary information and eliminating it. Furthermore, we discuss possible applications of the proposed framework: reducing channel information for speaker identification and domain information for unsupervised domain adaptation.

VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Short Learning    

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To extract and relate visual and linguistic concepts from images and textual descriptions for text-based zero-shot learning (ZSL), we develop variational hetero-encoder (VHE) that decodes text via a deep probabilisitic topic model, the variational posterior of whose local latent variables is encoded from an image via a Weibull distribution based inference network. To further improve VHE and add an image generator, we propose VHE randomized generative adversarial net (VHEGAN) that exploits the synergy between VHE and GAN through their shared latent space. After training with a hybrid stochastic-gradient MCMC/variational inference/stochastic gradient descent inference algorithm, VHEGAN can be used in a variety of settings, such as text generation/retrieval conditioning on an image, image generation/retrieval conditioning on a document/image, and generation of text-image pairs. The efficacy of VHEGAN is demonstrated quantitatively with experiments on both conventional and generalized ZSL tasks, and qualitatively on (conditional) image and/or text generation/retrieval.

DARTS: Differentiable Architecture Search    

tl;dr We propose a differentiable architecture search algorithm for both convolutional and recurrent networks, achieving competitive performance with the state of the art using orders of magnitude less computation resources.

This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques.

Hyper-Regularization: An Adaptive Choice for the Learning Rate in Gradient Descent    

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We present a novel approach for adaptively selecting the learning rate in gradient descent methods. Specifically, we impose a regularization term on the learning rate via a generalized distance, and cast the joint updating process of the parameter and the learning rate into a maxmin problem. Some existing schemes such as AdaGrad (diagonal version) and WNGrad can be rederived from our approach. Based on our approach, the updating rules for the learning rate do not rely on the smoothness constant of optimization problems and are robust to the initial learning rate. We theoretically analyze our approach in full batch and online learning settings, which achieves comparable performances with other first-order gradient-based algorithms in terms of accuracy as well as convergence rate.

The relativistic discriminator: a key element missing from standard GAN    

tl;dr Improving the quality and stability of GANs using a relativistic discriminator; IPM GANs (such as WGAN-GP) are a special case.

In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real. The generator is trained to increase the probability that fake data is real. We argue that it should also simultaneously decrease the probability that real data is real because 1) this would account for a priori knowledge that half of the data in the mini-batch is fake, 2) this would be observed with divergence minimization, and 3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs. We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data. We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average. We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs). We show that IPM-based GANs are a subset of RGANs which use the identity function. Empirically, we observe that 1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts, 2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and 3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.

Distributionally Robust Optimization Leads to Better Generalization: on SGD and Beyond    

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In this paper, we adopt distributionally robust optimization (DRO) in hope to achieve a better generalization in deep learning tasks. We establish the generalization guarantees and analyze the localized Rademacher complexity for DRO, and conduct experiments to show that DRO obtains a better performance. We reveal the profound connection between SGD and DRO, i.e., selecting a batch can be viewed as choosing a distribution over the training set. From this perspective, we prove that SGD is prone to escape from bad stationary points and small batch SGD outperforms large batch SGD. We give an upper bound for the robust loss when SGD converges and keeps stable. We propose a novel Weighted SGD (WSGD) algorithm framework, which assigns high-variance weights to the data of the current batch. We devise a practical implement of WSGD that can directly optimize the robust loss. We test our algorithm on CIFAR-10 and CIFAR-100, and WSGD achieves significant improvements over the conventional SGD.

Quasi-hyperbolic momentum and Adam for deep learning    

tl;dr Mix plain SGD and momentum (or do something similar with Adam) for great profit.

Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover. Finally, we propose a QH variant of Adam called QHAdam, and we empirically demonstrate that our algorithms lead to significantly improved training in a variety of settings, including a new state-of-the-art result on WMT16 EN-DE. We hope that these empirical results, combined with the conceptual and practical simplicity of QHM and QHAdam, will spur interest from both practitioners and researchers. PyTorch code is immediately available.

Latent Transformations for View Synthesis with Conditional Convolutional Networks    

tl;dr We introduce an effective, general framework for incorporating conditioning information into inference-based generative models.

We propose a fully-convolutional conditional generative model, the latent transformation neural network (LTNN), capable of view synthesis using a light-weight neural network suited for real-time applications. In contrast to existing conditional generative models which incorporate conditioning information via concatenation, we introduce a dedicated network component, the conditional transformation unit (CTU), designed to learn the latent space transformations corresponding to specified target views. In addition, a consistency loss term is defined to guide the network toward learning the desired latent space mappings, a task-divided decoder is constructed to refine the quality of generated views, and an adaptive discriminator is introduced to improve the adversarial training process. The generality of the proposed methodology is demonstrated on a collection of three diverse tasks: multi-view reconstruction on real hand depth images, view synthesis of real and synthetic faces, and the rotation of rigid objects. The proposed model is shown to exceed state-of-the-art results in each category while simultaneously achieving a reduction in the computational demand required for inference by 30% on average.

Reducing Overconfident Errors outside the Known Distribution    

tl;dr Deep networks are more likely to be confidently wrong when testing on unexpected data. We propose two methods to reduce confident errors on unknown input distributions, and an experimental methodology to study the problem.

Intuitively, unfamiliarity should lead to lack of confidence. In reality, current algorithms often make highly confident yet wrong predictions when faced with unexpected test samples from an unknown distribution different from training. Unlike domain adaptation methods, we cannot gather an "unexpected dataset" prior to test, and unlike novelty detection methods, a best-effort original task prediction is still expected. We propose two simple solutions that reduce overconfident errors of samples from an unknown novel distribution without drastically increasing evaluation time: (1) G-distillation, training an ensemble of classifiers and then distill into a single model using both labeled and unlabeled examples, or (2) NCR, reducing prediction confidence based on its novelty detection score. Experimentally, we investigate the overconfidence problem and evaluate our solution by creating "familiar" and "novel" test splits, where "familiar" are identically distributed with training and "novel" are not. We show that our solution yields more appropriate prediction confidences, on familiar and novel data, compared to single models and ensembles distilled on training data only. For example, our G-distillation reduces confident errors in gender recognition by 94% on demographic groups different from the training data.

Bayesian Deep Learning via Stochastic Gradient MCMC with a Stochastic Approximation Adaptation    

tl;dr a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables

We propose a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables. Inspired by dropout, a popular tool for regularization and model ensemble, we assign sparse priors to the weights in deep neural networks (DNN) in order to achieve automatic ``dropout'' and avoid over-fitting. By alternatively sampling from posterior distribution through stochastic gradient Markov Chain Monte Carlo (SG-MCMC) and optimizing latent variables via stochastic approximation (SA), the trajectory of the target weights is proved to converge to the true posterior distribution conditioned on optimal latent variables. This ensures a stronger regularization on the over-fitted parameter space and more accurate uncertainty quantification on the decisive variables. Simulations from large-p-small-n regressions showcase the robustness of this method when applied to models with latent variables. Additionally, its application on the convolutional neural networks (CNN) leads to state-of-the-art performance on MNIST and Fashion MNIST datasets and improved resistance to adversarial attacks.

A Direct Approach to Robust Deep Learning Using Adversarial Networks    

tl;dr Jointly train an adversarial noise generating network with a classification network to provide better robustness to adversarial attacks.

Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly sensitive to small perturbations imperceptible to humans. Carefully crafted input images (adversarial examples) can force a well-trained neural network to provide arbitrary outputs. Including adversarial examples during training is a popular defense mechanism against adversarial attacks. In this paper we propose a new defensive mechanism under the generative adversarial network~(GAN) framework. We model the adversarial noise using a generative network, trained jointly with a classification discriminative network as a minimax game. We show empirically that our adversarial network approach works well against black box attacks, with performance on par with state-of-art methods such as ensemble adversarial training and adversarial training with projected gradient descent.

Improved Gradient Estimators for Stochastic Discrete Variables    

tl;dr We propose simple ways to reduce bias and complexity of stochastic gradient estimators used for learning distributions over discrete variables.

In many applications we seek to optimize an expectation with respect to a distribution over discrete variables. Estimating gradients of such objectives with respect to the distribution parameters is a challenging problem. We analyze existing solutions including finite-difference (FD) estimators and continuous relaxation (CR) estimators in terms of bias and variance. We show that the commonly used Gumbel-Softmax estimator is biased and propose a simple method to reduce it. We also derive a simpler piece-wise linear continuous relaxation that also possesses reduced bias. We demonstrate empirically that reduced bias leads to a better performance in variational inference and on binary optimization tasks.

Combinatorial Attacks on Binarized Neural Networks    

tl;dr Gradient-based attacks on binarized neural networks are not effective due to the non-differentiability of such networks; Our IPROP algorithm solves this problem using integer optimization

Binarized Neural Networks (BNNs) have recently attracted significant interest due to their computational efficiency. Concurrently, it has been shown that neural networks may be overly sensitive to "attacks" -- tiny adversarial changes in the input -- which may be detrimental to their use in safety-critical domains. Designing attack algorithms that effectively fool trained models is a key step towards learning robust neural networks. The discrete, non-differentiable nature of BNNs, which distinguishes them from their full-precision counterparts, poses a challenge to gradient-based attacks. In this work, we study the problem of attacking a BNN through the lens of combinatorial and integer optimization. We propose a Mixed Integer Linear Programming (MILP) formulation of the problem. While exact and flexible, the MILP quickly becomes intractable as the network and perturbation space grow. To address this issue, we propose IProp, a decomposition-based algorithm that solves a sequence of much smaller MILP problems. Experimentally, we evaluate both proposed methods against the standard gradient-based attack (FGSM) on MNIST and Fashion-MNIST, and show that IProp performs favorably compared to FGSM, while scaling beyond the limits of the MILP.

Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency    

tl;dr We propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain.

Image-to-image translation has recently received significant attention due to advances in deep learning. Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way. However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations. To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain. We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain. Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain. To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels. Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.

Accidental explorationa through value predictors    

tl;dr We study the biases introduced in common value predictors by the fact that trajectories are, in practice, finite.

Infinite length of trajectories is an almost universal assumption in the theoretical foundations of reinforcement learning. Of course, in practice this is never the case. In this paper we examine a specific result of this disparity. We focus on the case where the finiteness of trajectories also makes the underlying process to lose the Markov property. This causes the standard state value estimators to become biased, which in turn manifests as a vastly different learning dynamic when algorithms use value predictors. We investigate these claims theoretically for a one dimensional random walk and Wiener process, and empirically on a number of simple environments. We use GAE as an algorithm which uses a value predictor and compare it to a plain policy gradient.

TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer    

tl;dr We present the TimbreTron, a pipeline for perfoming high-quality timbre transfer on musicalwaveforms using CQT-domain style transfer.

In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation. We introduce TimbreTron, an audio processing pipeline which combines three powerful ideas from different domains: Constant Q Transform (CQT) spectrogram for audio representation, a variant of CycleGAN for timbre transfer and WaveNet-Synthesizer for high quality audio generation. We verified that CQT TimbreTron in principle and in practice is more suitable than its STFT counterpart, even though STFT is more commonly used for audio representation. Based on human perceptual evaluations, we confirmed that timbre was transferred recognizably while the musical content was preserved by TimbreTron.

Whitening and Coloring transform for GANs    

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Batch Normalization (BN) is a common technique used to speed-up and stabilize training. On the other hand, the learnable parameters of BN are commonly used in conditional Generative Adversarial Networks (cGANs) for representing class-specific information using conditional Batch Normalization (cBN). In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization. We show that our conditional Coloring can represent categorical conditioning information which largely helps the cGAN qualitative results. Moreover, we show that full-feature whitening is important in a general GAN scenario in which the training process is known to be highly unstable. We test our approach on different datasets and using different GAN networks and training protocols, showing a consistent improvement in all the tested frameworks. Our CIFAR-10 supervised results are higher than all previous works on this dataset.

Learning Diverse Generations using Determinantal Point Processes    

tl;dr The addition of a diversity criterion inspired from DPP in the GAN objective avoids mode collapse and leads to better generations.

Generative models have proven to be an outstanding tool for representing high- dimensional probability distributions and generating realistic looking images. A fundamental characteristic of generative models is their ability to produce multi- modal outputs. However while training, they are often susceptible to mode col- lapse, which means that the model is limited in mapping the input noise to only a few modes of the true data distribution. In this paper, we draw inspiration from Determinantal Point Process (DPP) to devise a generative model that alleviates mode collapse problem while producing higher quality samples. DPP is an ele- gant probabilistic measure used to model negative correlations within a subset, and hence quantify its diversity. We propose a generation penalty term that encourages the generator to behave as a Determinantal Point Process sampler and hence learns to generates diverse data. In contrast to previous state-of-the-art generative mod- els that tend to use additional trainable parameters or complex training paradigms, our method does not change the original training scheme. Embedded in an ad- versarial strategy, our Generative DPP approach shows a consistent resistance to mode-collapse on a wide-variety of synthetic data and natural image datasets in- cluding MNIST and CIFAR10, while outperforming state-of-the-art methods for data-efficiency, convergence-time, and generation quality. Our code will be made publicly available.

Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization    

tl;dr We describe a dynamic sparse reparameterization technique that allow training of a small sparse network to generalize on par with, or better than, a full-sized dense model compressed to the same size.

Modern deep neural networks are highly overparameterized, and often of huge sizes. A number of post-training model compression techniques, such as distillation, pruning and quantization, can reduce the size of network parameters by a substantial fraction with little loss in performance. However, training a small network of the post-compression size de novo typically fails to reach the same level of accuracy achieved by compression of a large network, leading to a widely-held belief that gross overparameterization is essential to effective learning. In this work, we argue that this is not necessarily true. We describe a dynamic sparse reparameterization technique that closed the performance gap between a model compressed by pruning and a model of the post-compression size trained de novo. We applied our method to training deep residual networks and showed that it outperformed existing static reparameterization techniques, yielding the best accuracy for a given parameter budget for training. Compared to other dynamic reparameterization methods that reallocate non-zero parameters during training, our approach broke free from a few key limitations and achieved much better performance at lower computational cost. Our method is not only of practical value for training under stringent memory constraints, but also potentially informative to theoretical understanding of generalization properties of overparameterized deep neural networks.

Learnable Embedding Space for Efficient Neural Architecture Compression    

tl;dr We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during neural architecture search (NAS).

We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during neural architecture search (NAS). In this paper, we focus on the task of network architecture compression. Given a teacher network, we search for a compressed network architecture by using Bayesian Optimization (BO) with a kernel function defined over our proposed embedding space to select architectures for evaluation. We demonstrate that our search algorithm can significantly outperform various baseline methods, such as random search and N2N (Ashok et al.,2018). The compressed architectures found by our method are also better than the state-of-the-art manually-designed compact architecture ShuffleNet (Zhang et al., 2018). We also demonstrate that the learned embedding space can be transferred to new settings for architecture search, such as a larger teacher network or a teacher network in a different architecture family, without any training.

Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images    

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The human ability to recognize objects is impaired when the object is not shown in full. "Minimal images" are the smallest regions of an image that remain recognizable for humans. Ullman et al. (2016) show that a slight modification of the location and size of the visible region of the minimal image produces a sharp drop in human recognition accuracy. In this paper, we demonstrate that such drops in accuracy due to changes of the visible region are a common phenomenon between humans and existing state-of-the-art deep neural networks (DNNs), and are much more prominent in DNNs. We found many cases where DNNs classified one region correctly and the other incorrectly, though they only differed by one row or column of pixels, and were often bigger than the average human minimal image size. We show that this phenomenon is independent from previous works that have reported lack of invariance to minor modifications in object location in DNNs. Our results thus reveal a new failure mode of DNNs that also affects humans to a much lesser degree. They expose how fragile DNN recognition ability is in natural images even without adversarial patterns being introduced. Bringing the robustness of DNNs in natural images to the human level remains an open challenge for the community.

Graph Matching Networks for Learning the Similarity of Graph Structured Objects    

tl;dr We tackle the problem of similarity learning for structured objects with applications in particular in computer security, and propose a new model graph matching networks that excels on this task.

This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to produce embedding of graphs in vector spaces that enables efficient similarity reasoning. Second, we propose a novel Graph Matching Network model that, given a pair of graphs as input, computes a similarity score between them by jointly reasoning on the pair through a new cross-graph attention-based matching mechanism. We demonstrate the effectiveness of our models on different domains including the challenging problem of control-flow-graph based function similarity search that plays an important role in the detection of vulnerabilities in software systems. The experimental analysis demonstrates that our models are not only able to exploit structure in the context of similarity learning but they can also outperform domain-specific baseline systems that have been carefully hand-engineered for these problems.

Improving Sentence Representations with Multi-view Frameworks    

tl;dr Multi-view learning improves unsupervised sentence representation learning

Multi-view learning can provide self-supervision when different views are available of the same data. Distributional hypothesis provides another form of useful self-supervision from adjacent sentences which are plentiful in large unlabelled corpora. Motivated by the asymmetry in the two hemispheres of the human brain as well as the observation that different learning architectures tend to emphasise different aspects of sentence meaning, we present two multi-view frameworks for learning sentence representations in an unsupervised fashion. One framework uses a generative objective and the other a discriminative one. In both frameworks, the final representation is an ensemble of two views, in which, one view encodes the input sentence with a Recurrent Neural Network (RNN), and the other view encodes it with a simple linear model. We show that, after learning, the vectors produced by our multi-view frameworks provide improved representations over their single-view learned counterparts, and the combination of different views gives representational improvement over each view and demonstrates solid transferability on standard downstream tasks.

Composing Entropic Policies using Divergence Correction    

tl;dr Two new methods for combining entropic policies: maximum entropy generalized policy improvement, and divergence correction.

Deep reinforcement learning (RL) algorithms have made great strides in recent years. An important remaining challenge is the ability to quickly transfer existing skills to novel tasks, and to combine existing skills with newly acquired ones. In domains where tasks are solved by composing skills this capacity holds the promise of dramatically reducing the data requirements of deep RL algorithms, and hence of greatly increasing their applicability. Recent work has studied ways of composing behaviors represented in the form of action-value functions. We analyze these methods to highlight their strengths and weaknesses, and point out situations where each of them is susceptible to poor performance. To perform this analysis we extend generalized policy improvement to the max-entropy framework and introduce a method for the practical implementation of successor features in continuous action spaces. Then we propose a novel approach which achieves an approximately optimal result. This method works by explicitly learning the (discounted, future) divergence between policies. We study this approach in the tabular case and propose a scalable variant that is applicable in multi-dimensional continuous action spaces. We compare our novel approach with existing ones on a range of non-trivial continuous control problems with compositional structure, and demonstrate near-optimal performance despite requiring less information than competing approaches.

Bayesian Policy Optimization for Model Uncertainty    

tl;dr We formulate model uncertainty in Reinforcement Learning as a continuous Bayes-Adaptive Markov Decision Process and present a method for practical and scalable Bayesian policy optimization.

Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov Decision Process (BAMDP), where an agent maintains a posterior distribution over the latent model parameters given a history of observations and maximizes its expected long-term reward with respect to this belief distribution. Our algorithm, Bayesian Policy Optimization, builds on recent policy optimization algorithms to learn a universal policy that navigates the exploration-exploitation trade-off to maximize the Bayesian value function. To address challenges from discretizing the continuous latent parameter space, we propose a policy network architecture that independently encodes the belief distribution from the observable state. Our method significantly outperforms algorithms that address model uncertainty without explicitly reasoning about belief distributions, and is competitive with state-of-the-art Partially Observable Markov Decision Process solvers.


tl;dr We propose a new variational inference algorithm for time series and a novel variational family endowed with nonlinear dynamics.

Latent variable models have been widely applied for the analysis and visualization of large datasets. In the case of sequential data, closed-form inference is possible when the transition and observation functions are linear. However, approximate inference techniques are usually necessary when dealing with nonlinear dynamics and observation functions. Here, we propose a novel variational inference frame- work for the explicit modeling of time series, Variational Inference for Nonlinear Dynamics (VIND), that is able to uncover nonlinear observation and transition functions from sequential data. The framework includes a structured approximate posterior, and an algorithm that relies on the fixed-point iteration method to find the best estimate for latent trajectories. We apply the method to several datasets and show that it is able to accurately infer the underlying dynamics of these systems, in some cases substantially outperforming state-of-the-art methods.


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This paper revisits the Random Walk model for sentence embedding in the context of non-extensive statistics. We propose a non-extensive algebra to compute the discourse vector. We argue that by doing so we are taking into account high non-linearity in the semantic space. Furthermore, we show that by considering a non-extensive algebra, the compounding effect of the vector length is mitigated. Overall, we show that the proposed model leads to good sentence embedding. We evaluate the embedding method on textual similarity tasks.

On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks    

tl;dr This paper proves the universal approximability of quantized ReLU neural networks and puts forward the complexity bound given arbitrary error.

Compression is a key step to deploy large neural networks on resource-constrained platforms. As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight. In this paper, we study the representation power of quantized neural networks. First, we prove the universal approximability of quantized ReLU networks on a wide class of functions. Then we provide upper bounds on the number of weights and the memory size for a given approximation error bound and the bit-width of weights for function-independent and function-dependent structures. Our results reveal that, to attain an approximation error bound of $\epsilon$, the number of weights needed by a quantized network is no more than $\mathcal{O}\left(\log^5(1/\epsilon)\right)$ times that of an unquantized network. This overhead is of much lower order than the lower bound of the number of weights needed for the error bound, supporting the empirical success of various quantization techniques. To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.

Traditional and Heavy Tailed Self Regularization in Neural Network Models    

tl;dr See the abstract.

Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly indicate that the empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of regularization, such as Dropout or Weight Norm constraints. Building on recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify 5+1 Phases of Training, corresponding to increasing amounts of Implicit Self-Regularization. For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a "size scale" separating signal from noise. For state-of-the-art DNNs, however, we identify a novel form of Heavy-Tailed Self-Regularization, similar to the self-organization seen in the statistical physics of disordered systems. This implicit Self-Regularization can depend strongly on the many knobs of the training process. By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size.


tl;dr Develop new techniques that rely on patch reordering to enable detailed analysis of data-set relationship to training and generalization performances.

We present a framework for automatically ordering image patches that enables in-depth analysis of dataset relationship to learnability of a classification task using convolutional neural network. An image patch is a group of pixels residing in a continuous area contained in the sample. Our preliminary experimental results show that an informed smart shuffling of patches at a sample level can expedite training by exposing important features at early stages of training. In addition, we conduct systematic experiments and provide evidence that CNN’s generalization capabilities do not correlate with human recognizable features present in training samples. We utilized the framework not only to show that spatial locality of features within samples do not correlate with generalization, but also to expedite convergence while achieving similar generalization performance. Using multiple network architectures and datasets, we show that ordering image regions using mutual information measure between adjacent patches, enables CNNs to converge in a third of the total steps required to train the same network without patch ordering.

Learning Localized Generative Models for 3D Point Clouds via Graph Convolution    

tl;dr A GAN using graph convolution operations with dynamically computed graphs from hidden features

Point clouds are an important type of geometric data and have widespread use in computer graphics and vision. However, learning representations for point clouds is particularly challenging due to their nature as being an unordered collection of points irregularly distributed in 3D space. Graph convolution, a generalization of the convolution operation for data defined over graphs, has been recently shown to be very successful at extracting localized features from point clouds in supervised or semi-supervised tasks such as classification or segmentation. This paper studies the unsupervised problem of a generative model exploiting graph convolution. We focus on the generator of a GAN and define methods for graph convolution when the graph is not known in advance as it is the very output of the generator. The proposed architecture learns to generate localized features that approximate graph embeddings of the output geometry. We also study the problem of defining an upsampling layer in the graph-convolutional generator, whereby it learns to exploit a self-similarity prior to sample the data distribution.

Variadic Learning by Bayesian Nonparametric Deep Embedding    

tl;dr We address any-shot, any-way learning with multi-modal prototypes by connecting bayesian nonparametrics and deep metric learning

Learning at small or large scales of data is addressed by two strong but divided frontiers: few-shot learning and standard supervised learning. Few-shot learning focuses on sample efficiency at small scale, while supervised learning focuses on accuracy at large scale. Ideally they could be reconciled for effective learning at any number of data points (shot) and number of classes (way). To span the full spectrum of shot and way, we frame the variadic learning regime of learning from any number of inputs. We approach variadic learning by meta-learning a novel multi-modal clustering model that connects bayesian nonparametrics and deep metric learning. Our bayesian nonparametric deep embedding (BANDE) method is optimized end-to-end with a single objective, and adaptively adjusts capacity to learn from variable amounts of supervision. BANDE achieves a) state-of-the-art results on semi-supervised classification of Omniglot and mini-ImageNet, b)impressive 75% classification accuracy on the 1692-way, 10-shot classification task of Omniglot while only training for 5-way 1-shot classification, c)94.37% accuracy on CIFAR-10 by episodic optimization, comparable to state-of-the-art supervised learning techniques, and d) strong unsupervised clustering performance, with the ability to discover character classes given no character supervision.

BNN+: Improved Binary Network Training    

tl;dr The paper presents an improved training mechanism for obtaining binary networks with smaller accuracy drop compared that helps close the gap with it's full precision counterpart

Deep neural networks (DNN) are widely used in many applications. However, their deployment on edge devices has been difficult because they are resource hungry. Binary networks (BNN) help to alleviate the prohibitive resource requirements of DNN; where both activations and weights are limited to one bit. We propose an improved binary training method (BNN+), an improvement to the popular BNN training scheme, which helps to reduce accuracy degradation compared to the full-precision counterpart. Our method is based on linear operations that are easily implementable into the binary training framework and we show experimental results on CIFAR-10 obtaining an accuracy of 86.5%, on AlexNet and 91.6% with VGG network. On ImageNet, our method also outperforms the traditional BNN and XNOR-net, by a margin of 4% and 2% respectively.


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Langevin diffusion is a powerful method for nonconvex optimization, which enables the escape from local minima by injecting noise into the gradient. In particular, the temperature parameter controlling the noise level gives rise to a tradeoff between ``global exploration'' and ``local exploitation'', which correspond to high and low temperatures. To attain the advantages of both regimes, we propose to use replica exchange, which swaps between two Langevin diffusions with different temperatures. We theoretically analyze the acceleration effect of replica exchange from two perspectives: (i) the convergence in $\chi^2$-divergence, and (ii) the large deviation principle. Such an acceleration effect allows us to faster approach the global minima. Furthermore, by discretizing the replica exchange Langevin diffusion, we obtain a discrete-time algorithm. For such an algorithm, we quantify its discretization error in theory and demonstrate its acceleration effect in practice.

Explaining Neural Networks Semantically and Quantitatively    

tl;dr This paper presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically.

This paper presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically. How to analyze the specific rationale of each prediction made by the CNN presents one of key issues of understanding neural networks, but it is also of significant practical values in certain applications. In this study, we propose to distill knowledge from the CNN into an explainable additive model, so that we can use the explainable model to provide a quantitative explanation for the CNN prediction. We analyze the typical bias-interpreting problem of the explainable model and develop prior losses to guide the learning of the explainable additive model. Experimental results have demonstrated the effectiveness of our method.

On the Convergence and Robustness of Batch Normalization    

tl;dr We mathematically analyze the effect of batch normalization on a simple model and obtain key new insights that applies to general supervised learning.

Despite its empirical success, the theoretical underpinnings of the stability, convergence and acceleration properties of batch normalization (BN) remain elusive. In this paper, we attack this problem from a modelling approach, where we perform thorough theoretical analysis on BN applied to simplified model: ordinary least squares (OLS). We discover that gradient descent on OLS with BN has interesting properties, including a scaling law, convergence for arbitrary learning rates for the weights, asymptotic acceleration effects, as well as insensitivity to choice of learning rates. We then demonstrate numerically that these findings are not specific to the OLS problem and hold qualitatively for more complex supervised learning problems. This points to a new direction towards uncovering the mathematical principles that underlies batch normalization.

Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering    

tl;dr We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features.

We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. In general, our superstructure is a tree structure of multiple super latent variables and it is automatically learned from data. When there is only one latent variable in the superstructure, our model reduces to one that assumes the latent features to be generated from a Gaussian mixture model. We call our model the latent tree variational autoencoder (LTVAE). Whereas previous deep learning methods for clustering produce only one partition of data, LTVAE produces multiple partitions of data, each being given by one super latent variable. This is desirable because high dimensional data usually have many different natural facets and can be meaningfully partitioned in multiple ways.

Learning and Planning with a Semantic Model    

tl;dr We propose a hybrid model-based & model-free approach using semantic information to improve DRL generalization in man-made environments.

Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.

Variational Autoencoders with Jointly Optimized Latent Dependency Structure    

tl;dr We propose a method for learning latent dependency structure in variational autoencoders.

We propose a method for learning the dependency structure between latent variables in deep latent variable models. Our general modeling and inference framework combines the complementary strengths of deep generative models and probabilistic graphical models. In particular, we express the latent variable space of a variational autoencoder (VAE) in terms of a Bayesian network with a learned, flexible dependency structure. The network parameters, variational parameters as well as the latent topology are optimized simultaneously with a single variational objective. Inference is formulated via a sampling procedure that produces expectations over latent variable structures and incorporates top-down and bottom-up reasoning over latent variable values. We validate our framework in extensive experiments on MNIST, Omniglot, and CIFAR-10. Comparisons to state-of-the-art structured variational autoencoder baselines show improvements in terms of the expressiveness of the learned model.

The Unusual Effectiveness of Averaging in GAN Training    

No tl;dr =[

We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) computes an exponentially discounted sum. Whilst MA is known to lead to convergence in bilinear settings, we provide the first to our knowledge theoretical arguments in support of EMA. We show that EMA converges to limit cycles around the equilibrium with vanishing amplitude as the discount parameter approaches one. We establish experimentally that both techniques are strikingly effective in the non convex-concave GAN setting as well. Both improve inception and FID scores on different architectures and for different GAN objectives. We provide comprehensive experimental results across a range of datasets -- mixture of Gaussians, CIFAR-10, STL-10, CelebA and ImageNet -- to demonstrate its effectiveness. We achieve state-of-the-art results on CIFAR-10 and produce clean CelebA face images.


tl;dr We present a single shot analysis of a trained neural network to remove redundancy and identify optimal network structure

Deep learning models have outperformed traditional methods in many fields such as natural language processing and computer vision. However, despite their tremendous success, the methods of designing optimal Convolutional Neural Networks (CNNs) are still based on heuristics or grid search. The resulting networks obtained using these techniques are often overparametrized with huge computational and memory requirements. This paper focuses on a structured, explainable approach towards optimal model design that maximizes accuracy while keeping computational costs tractable. We propose a single-shot analysis of a trained CNN that uses Principal Component Analysis (PCA) to determine the number of filters that are doing significant transformations per layer, without the need for retraining. It can be interpreted as identifying the dimensionality of the hypothesis space under consideration. The proposed technique also helps estimate an optimal number of layers by looking at the expansion of dimensions as the model gets deeper. This analysis can be used to design an optimal structure of a given network on a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate these techniques by optimizing VGG and AlexNet networks on CIFAR-10, CIFAR-100 and ImageNet datasets.


tl;dr A new knowledge distill method for transfer learning

Although deep neural networks show their extraordinary power in various tasks, they are not feasible for deploying such large models on embedded systems due to high computational cost and storage space limitation. The recent work knowledge distillation (KD) aims at transferring model knowledge from a well-trained teacher model to a small and fast student model which can significantly help extending the usage of large deep neural networks on portable platform. In this paper, we show that, by properly defining the neuron manifold of deep neuron network (DNN), we can significantly improve the performance of student DNN networks through approximating neuron manifold of powerful teacher network. To make this, we propose several novel methods for learning neuron manifold from DNN model. Empowered with neuron manifold knowledge, our experiments show the great improvement across a variety of DNN architectures and training data. Compared with other KD methods, our Neuron Manifold Transfer (NMT) has best transfer ability of the learned features.

D-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation    

tl;dr A new two-stage positive unlabeled learning approach with GAN

Positive Unlabeled learning task remains an interesting challenge in the context of image analysis. Recent approaches suggest to exploit the GANs abilities to answer this problem. In this paper, we propose a new approach named Divergent-GAN (D-GAN). It keeps the light adversarial architecture of the PGAN method, with a better robustness counter the varying images complexity, while simultaneously allowing the same functionalities as the GenPU method, like the generation of relevant counter-examples. However, this is achieved without the need of prior knowledge, nor an onerous architecture and framework. Its functionning is based on the combination between the behaviour principles of Positive Unlabeled learning classification and the adversarial GAN training. Experimental results show that this divergent adversarial framework outperforms the state of the art PU learning in terms of prediction accuracy, training robustness, and its ability to work on both simple and complex real images. Combined with an additional generator, the proposed approach even allows to accomplish noisy labeled learning, and thus opening new application perspectives for GANs architectures.

Selective Convolutional Units: Improving CNNs via Channel Selectivity    

tl;dr We propose a new module that improves any ResNet-like architectures by enforcing "channel selective" behavior to convolutional layers

Bottleneck structures with identity (e.g., residual) connection are now emerging popular paradigms for designing deep convolutional neural networks (CNN), for processing large-scale features efficiently. In this paper, we focus on the information-preserving nature of the bottleneck structures and utilize this to enable a convolutional layer to have a new functionality of channel-selectivity, i.e., focusing its computations on important channels. In particular, we propose Selective Convolutional Unit (SCU), an easy-to-use architectural unit that improves parameter efficiency of various modern CNNs with bottlenecks. During training, SCU gradually learns the channel-selectivity on-the-fly via the alternative usage of (a) pruning unimportant channels, and (b) rewiring the pruned parameters to important channels. The rewired parameters emphasize the target channel in a way that selectively enlarges the convolutional kernels corresponding to it. Our experimental results demonstrate that the SCU-based models without any post-processing generally achieve both model compression and accuracy improvement compared to the baselines, consistently for all tested architectures.

Functional Bayesian Neural Networks for Model Uncertainty Quantification    

No tl;dr =[

In this paper, we extend the Bayesian neural network to functional Bayesian neural network with functional Monte Carlo methods that use the samples of functionals instead of samples of networks' parameters for inference to overcome the curse of dimensionality for uncertainty quantification. Based on the previous work on Riemannian Langevin dynamics, we propose the stochastic gradient functional Riemannian dynamics for training functional Bayesian neural network. We show the effectiveness and efficiency of our proposed approach with various experiments.

Generating Images from Sounds Using Multimodal Features and GANs    

tl;dr We propose a method of converting from the sound domain into the image domain based on multimodal features and stacked GANs.

Although generative adversarial networks (GANs) have enabled us to convert images from one domain to another similar one, converting between different sensory modalities, such as images and sounds, has been difficult. This study aims to propose a network that reconstructs images from sounds. First, video data with both images and sounds are labeled with pre-trained classifiers. Second, image and sound features are extracted from the data using pre-trained classifiers. Third, multimodal layers are introduced to extract features that are common to both the images and sounds. These layers are trained to extract similar features regardless of the input modality, such as images only, sounds only, and both images and sounds. Once the multimodal layers have been trained, features are extracted from input sounds and converted into image features using a feature-to-feature GAN. Finally, the generated image features are used to reconstruct images. Experimental results show that this method can successfully convert from the sound domain into the image domain. When we applied a pre-trained classifier to both the generated and original images, 31.9% of the examples had at least one of their top 10 labels in common, suggesting reasonably good image generation. Our results suggest that common representations can be learned for different modalities, and that proposed method can be applied not only to sound-to-image conversion but also to other conversions, such as from images to sounds.

Empirical observations on the instability of aligning word vector spaces with GANs    

tl;dr An empirical investigation of GAN-based alignment of word vector spaces, focusing on cases, where linear transformations provably exist, but training is unstable.

Unsupervised bilingual dictionary induction (UBDI) is useful for unsupervised machine translation and for cross-lingual transfer of models into low-resource languages. One approach to UBDI is to align word vector spaces in different languages using Generative adversarial networks (GANs) with linear generators, achieving state-of-the-art performance for several language pairs. For some pairs, however, GAN-based induction is unstable or completely fails to align the vector spaces. We focus on cases where linear transformations provably exist, but the performance of GAN-based UBDI depends heavily on the model initialization. We show that the instability depends on the shape and density of the vector sets, but not on noise; it is the result of local optima, but neither over-parameterization nor changing the batch size or the learning rate consistently reduces instability. Nevertheless, we can stabilize GAN-based UBDI through best-of-N model selection, based on an unsupervised stopping criterion.

Structured Prediction using cGANs with Fusion Discriminator    

tl;dr We propose a novel way to incorporate conditional image information into the discriminator of GANs using feature fusion that can be used for structured prediction tasks.

We propose a novel method for incorporating conditional information into a generative adversarial network (GAN) for structured prediction tasks. This method is based on fusing features from the generated and conditional information in feature space and allows the discriminator to better capture higher-order statistics from the data. This method also increases the strength of the signals passed through the network where the real or generated data and the conditional data agree. The proposed method is conceptually simpler than the joint convolutional neural network - conditional Markov random field (CNN-CRF) models and enforces higher-order consistency without being limited to a very specific class of high-order potentials. Experimental results demonstrate that this method leads to improvement on a variety of different structured prediction tasks including image synthesis, semantic segmentation, and depth estimation.

Learning sparse relational transition models    

tl;dr A new approach that learns a representation for describing transition models in complex uncertaindomains using relational rules.

We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state. Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties. This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table.

Multi-class classification without multi-class labels    

No tl;dr =[

This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction and through this process learns a multi-class classifier as a submodule. We formulate this approach, present a probabilistic graphical model for it, and derive a surprisingly simple loss function that can be used to learn neural network-based models. We then demonstrate that this same framework generalizes to the supervised, unsupervised cross-task, and semi-supervised settings. Our method is evaluated against state of the art in all three learning paradigms and shows a superior or comparable accuracy, providing evidence that learning multi-class classification without multi-class labels is a viable learning option.

Spatial-Winograd Pruning Enabling Sparse Winograd Convolution    

tl;dr To accelerate the computation of convolutional neural networks, we propose a new two-step pruning technique which achieves a higher Winograd-domain weight sparsity without changing the network structure.

Deep convolutional neural networks (CNNs) are deployed in various applications but demand immense computational requirements. Pruning techniques and Winograd convolution are two typical methods to reduce the CNN computation. However, they cannot be directly combined because Winograd transformation fills in the sparsity resulting from pruning. Li et al. (2017) propose sparse Winograd convolution in which weights are directly pruned in the Winograd domain, but this technique is not very practical because Winograd-domain retraining requires low learning rates and hence significantly longer training time. Besides, Liu et al. (2018) move the ReLU function into the Winograd domain, which can help increase the weight sparsity but requires changes in the network structure. To achieve a high Winograd-domain weight sparsity without changing network structures, we propose a new pruning method, spatial-Winograd pruning. As the first step, spatial-domain weights are pruned in a structured way, which efficiently transfers the spatial-domain sparsity into the Winograd domain and avoids Winograd-domain retraining. For the next step, we also perform pruning and retraining directly in the Winograd domain but propose to use an importance factor matrix to adjust weight importance and weight gradients. This adjustment makes it possible to effectively retrain the pruned Winograd-domain network without changing the network structure. For the three models on the datasets of CIFAR-10, CIFAR-100, and ImageNet, our proposed method can achieve the Winograd-domain sparsities of 63%, 50%, and 74%, respectively.

Generative Adversarial Network Training is a Continual Learning Problem    

tl;dr Generative Adversarial Network Training is a Continual Learning Problem.

Generative Adversarial Networks (GANs) have proven to be a powerful framework for learning to draw samples from complex distributions. However, GANs are also notoriously difficult to train, with mode collapse and oscillations a common problem. We hypothesize that this is at least in part due to the evolution of the generator distribution and the catastrophic forgetting tendency of neural networks, which leads to the discriminator losing the ability to remember synthesized samples from previous instantiations of the generator. Recognizing this, our contributions are twofold. First, we show that GAN training makes for a more interesting and realistic benchmark for continual learning methods evaluation than some of the more canonical datasets. Second, we propose leveraging continual learning techniques to augment the discriminator, preserving its ability to recognize previous generator samples. We show that the resulting methods add only a light amount of computation, involve minimal changes to the model, and result in better overall performance on the examined image and text generation tasks.

On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond    

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We propose a generalization error bound for a general family of deep neural networks based on the depth and width of the networks, as well as the spectral norm of weight matrices. Through introducing a novel characterization of the Lipschitz properties of neural network family, we achieve a tighter generalization error bound. We further obtain a result that is free of linear dependence on norms for bounded losses. Besides the general deep neural networks, our results can be applied to derive new bounds for several popular architectures, including convolutional neural networks (CNNs), residual networks (ResNets), and hyperspherical networks (SphereNets). When achieving same generalization errors with previous arts, our bounds allow for the choice of much larger parameter spaces of weight matrices, inducing potentially stronger expressive ability for neural networks.

Representation Degeneration Problem in Training Natural Language Generation Models    

No tl;dr =[

We study an interesting problem in training neural network-based models for natural language generation tasks, which we call the \emph{representation degeneration problem}. We observe that when we train a model in natural language generation tasks through likelihood maximization with weight tying trick, especially with big training dataset, most of the learnt word embeddings tend to degenerate and be distributed into a narrow cone, which largely limits the representation power of word embeddings. We analyze the conditions and causes of this problem and propose a novel regularization method to address it. Experiments on language modeling and machine translation show that our method can largely mitigate the representation degeneration problem and achieve better performance than baseline algorithms.


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Learning world dynamics has recently been investigated as a way to make reinforcement learning (RL) algorithms to be more sample efficient and interpretable. In this paper, we propose to capture an environment dynamics with a novel forward model that leverages recent works on adversarial learning and visual control. Such a model estimates future observations conditioned on the current ones and other input variables such as actions taken by an RL-agent. We focus on image generation which is a particularly challenging topic but our method can be adapted to other modalities. More precisely, our forward model is trained to produce realistic observations of the future while a discriminator model is trained to distinguish between real images and the model’s prediction of the future. This approach overcomes the need to define an explicit loss function for the forward model which is currently used for solving such a class of problem. As a consequence, our learning protocol does not have to rely on an explicit distance such as Euclidean distance which tends to produce unsatisfactory predictions. To illustrate our method, empirical qualitative and quantitative results are presented on a real driving scenario, along with qualitative results on Atari game Frostbite.

Generating Text through Adversarial Training using Skip-Thought Vectors    

tl;dr Generating text using sentence embeddings from Skip-Thought Vectors with the help of Generative Adversarial Networks.

In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.

Manifold Alignment via Feature Correspondence    

tl;dr We propose a method for aligning the latent features learned from different datasets using harmonic correlations.

We propose a novel framework for combining datasets via alignment of their associated intrinsic dimensions. Our approach assumes that the two datasets are sampled from a common latent space, i.e., they measure equivalent systems. Thus, we expect there to exist a natural (albeit unknown) alignment of the data manifolds associated with the intrinsic geometry of these datasets, which are perturbed by measurement artifacts in the sampling process. Importantly, we do not assume any individual correspondence (partial or complete) between data points. Instead, we rely on our assumption that a subset of data features have correspondence across datasets. We leverage this assumption to estimate relations between intrinsic manifold dimensions, which are given by diffusion map coordinates over each of the datasets. We compute a correlation matrix between diffusion coordinates of the datasets by considering graph (or manifold) Fourier coefficients of corresponding data features. We then orthogonalize this correlation matrix to form an isometric transformation between the diffusion maps of the datasets. Finally, we apply this transformation to the diffusion coordinates and construct a unified diffusion geometry of the datasets together. We show that this approach successfully corrects misalignment artifacts, and allows for integrated data.

Infinitely Deep Infinite-Width Networks    

tl;dr We propose a method for the construction of arbitrarily deep infinite-width networks, based on which we derive a novel weight initialisation scheme for finite-width networks and demonstrate its competitive performance.

Infinite-width neural networks have been extensively used to study the theoretical properties underlying the extraordinary empirical success of standard, finite-width neural networks. Nevertheless, until now, infinite-width networks have been limited to at most two hidden layers. To address this shortcoming, we study the initialisation requirements of these networks and show that the main challenge for constructing them is defining the appropriate sampling distributions for the weights. Based on these observations, we propose a principled approach to weight initialisation that correctly accounts for the functional nature of the hidden layer activations and facilitates the construction of arbitrarily many infinite-width layers, thus enabling the construction of arbitrarily deep infinite-width networks. The main idea of our approach is to iteratively reparametrise the hidden-layer activations into appropriately defined reproducing kernel Hilbert spaces and use the canonical way of constructing probability distributions over these spaces for specifying the required weight distributions in a principled way. Furthermore, we examine the practical implications of this construction for standard, finite-width networks. In particular, we derive a novel weight initialisation scheme for standard, finite-width networks that takes into account the structure of the data and information about the task at hand. We demonstrate the effectiveness of this weight initialisation approach on the MNIST, CIFAR-10 and Year Prediction MSD datasets.

Heated-Up Softmax Embedding    

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Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples. The problem is generally solved by learning an embedding, such that the samples of the same category are close (compact) while samples from different categories are far away (spread-out) in the embedding space. One popular way of generating such embeddings is to use the second-to-last layer of a deep neural network trained as a classifier with the softmax cross-entropy loss. In this paper, we show that training classifiers with different temperatures of the softmax function lead to different distributions of the embedding space. And finding a balance between the compactness, 'spread-out' and the generalization ability of the feature is critical in metric learning. Leveraging these insights, we propose a 'heating-up' strategy to train a classifier with increasing temperatures. Extensive experiments show that the proposed method achieves state-of-the-art embeddings on a variety of metric learning benchmarks.

Spreading vectors for similarity search    

tl;dr We learn a neural network that uniformizes the input distribution, which leads to competitive indexing performance in high-dimensional space

Discretizing floating-point vectors is a fundamental step of modern indexing methods. State-of-the-art techniques learn parameters of the quantizers on training data for optimal performance, thus adapting quantizers to the data. In this work, we propose to reverse this paradigm and adapt the data to the quantizer: we train a neural net whose last layers form a fixed parameter-free quantizer, such as pre-defined points of a sphere. As a proxy objective, we design and train a neural network that favors uniformity in the spherical latent space, while preserving the neighborhood structure after the mapping. For this purpose, we propose a new regularizer derived from the Kozachenko-Leonenko differential entropy estimator and combine it with a locality-aware triplet loss. Experiments show that our end-to-end approach outperforms most learned quantization methods, and is competitive with the state of the art on widely adopted benchmarks. Further more, we show that training without the quantization step results in almost no difference in accuracy, but yields a generic catalyser that can be applied with any subsequent quantization technique.

Linearizing Visual Processes with Deep Generative Models    

tl;dr We model non-linear visual processes as autoregressive noise via generative deep learning.

This work studies the problem of modeling non-linear visual processes by leveraging deep generative architectures for learning linear, Gaussian models of observed sequences. We propose a joint learning framework, combining a multivariate autoregressive model and deep convolutional generative networks. After justification of theoretical assumptions of inearization, we propose an architecture that allows Variational Autoencoders and Generative Adversarial Networks to simultaneously learn the non-linear observation as well as the linear state-transition model from a sequence of observed frames. Finally, we demonstrate our approach on conceptual toy examples and dynamic textures.

Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers    

tl;dr Approximating mean and variance of the NN output over noisy input / dropout / uncertain parameters. Analytic approximations for argmax, softmax and max layers.

Probabilistic Neural Networks take into account various sources of stochasticity: input noise, dropout, stochastic neurons, parameter uncertainties modeled as random variables. In this paper we revisit the feed-forward propagation method that allows one to estimate for each neuron its mean and variance w.r.t. mentioned sources of stochasticity. In contrast, standard NNs propagate only point estimates, discarding the uncertainty. Methods propagating also the variance have been proposed by several authors in different context. The presented view attempts to clarify the assumptions and derivation behind such methods, relate it to classical NNs and broaden the scope of its applicability. The main technical innovations are new posterior approximations for argmax and max-related transforms, that allows for applicability in networks with softmax and max-pooling layers as well as leaky ReLU activations. We evaluate the accuracy of the approximation and suggest a simple calibration. Applying the method to networks with dropout allows for faster training and gives improved test likelihoods without the need of sampling.

Measuring and regularizing networks in function space    

tl;dr It is cheap to measure distances in function space, and these distances aren't always proportional to the corresponding parameter distances.

To optimize a neural network one often thinks of optimizing its parameters, but it is ultimately a matter of optimizing the function that maps inputs to outputs. Since a change in the parameters might serve as a poor proxy for the change in the function, it is of some concern that primacy is given to parameters but that the correspondence has not been tested. Here, we show that it is simple and computationally feasible to calculate distances between functions in a $L^2$ Hilbert space. We examine how typical networks behave in this space, and compare how parameter $\ell^2$ distances compare to function $L^2$ distances between various points of an optimization trajectory. We find that the two distances are nontrivially related. In particular, the $L^2/\ell^2$ ratio decreases throughout optimization, reaching a steady value around when test error plateaus. We then investigate how the $L^2$ distance could be applied directly to optimization. We first propose that in multitask learning, one can avoid catastrophic forgetting by directly limiting how much the input/output function changes between tasks. Secondly, we propose a new learning rule that regularizes the distance a network can travel through $L^2$-space in any one update. This allows new examples to be learned in a way that minimally interferes with what has previously been learned. These applications demonstrate how one can measure and regularize function distances directly, without relying on parameters or local approximations like loss curvature.

Pay Less Attention with Lightweight and Dynamic Convolutions    

tl;dr Dynamic lightweight convolutions are competitive to self-attention on language tasks.

Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic. Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models. On the WMT'14 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU.

Probabilistic Model-Based Dynamic Architecture Search    

tl;dr We present an efficient neural network architecture search method based on stochastic natural gradient method via probabilistic modeling.

The architecture search methods for convolutional neural networks (CNNs) have shown promising results. These methods require significant computational resources, as they repeat the neural network training many times to evaluate and search the architectures. Developing the computationally efficient architecture search method is an important research topic. In this paper, we assume that the structure parameters of CNNs are categorical variables, such as types and connectivities of layers, and they are regarded as the learnable parameters. Introducing the multivariate categorical distribution as the underlying distribution for the structure parameters, we formulate a differentiable loss for the training task, where the training of the weights and the optimization of the parameters of the distribution for the structure parameters are coupled. They are trained using the stochastic gradient descent, leading to the optimization of the structure parameters within a single training. We apply the proposed method to search the architecture for two computer vision tasks: image classification and inpainting. The experimental results show that the proposed architecture search method is fast and can achieve comparable performance to the existing methods.

Episodic Curiosity through Reachability    

tl;dr We propose a novel model of curiosity based on episodic memory and the ideas of reachability which allows us to overcome the known "couch-potato" issues of prior work.

Rewards are sparse in the real world and most today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself --- thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. Such bonus is summed up with the real task reward --- making it possible for RL algorithms to learn from the combined reward. We propose a new curiosity method which uses episodic memory to form the novelty bonus. To determine the bonus, the current observation is compared with the observations in memory. Crucially, the comparison is done based on how many environment steps it takes to reach the current observation from those in memory --- which incorporates rich information about environment dynamics. This allows us to overcome the known "couch-potato" issues of prior work --- when the agent finds a way to instantly gratify itself by exploiting actions which lead to unpredictable consequences. We test our approach in visually rich 3D environments in ViZDoom and DMLab. In ViZDoom, our agent learns to successfully navigate to a distant goal at least 2 times faster than the state-of-the-art curiosity method ICM. In DMLab, our agent generalizes well to new procedurally generated levels of the game --- reaching the goal at least 2 times more frequently than ICM on test mazes with very sparse reward.


tl;dr Introduce an information theoretic viewpoint on the behavior of deep networks optimization processes and their generalization abilities

Understanding the groundbreaking performance of Deep Neural Networks is one of the greatest challenges to the scientific community today. In this work, we introduce an information theoretic viewpoint on the behavior of deep networks optimization processes and their generalization abilities. By studying the Information Plane, the plane of the mutual information between the input variable and the desired label, for each hidden layer. Specifically, we show that the training of the network is characterized by a rapid increase in the mutual information (MI) between the layers and the target label, followed by a longer decrease in the MI between the layers and the input variable. Further, we explicitly show that these two fundamental information-theoretic quantities correspond to the generalization error of the network, as a result of introducing a new generalization bound that is exponential in the representation compression. The analysis focuses on typical patterns of large-scale problems. For this purpose, we introduce a novel analytic bound on the mutual information between consecutive layers in the network. An important consequence of our analysis is a super-linear boost in training time with the number of non-degenerate hidden layers, demonstrating the computational benefit of the hidden layers.

Phase-Aware Speech Enhancement with Deep Complex U-Net    

tl;dr This paper proposes a novel complex masking method for speech enhancement along with a loss function for efficient phase estimation.

Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction. This is due to the difficulty of estimating the phase of clean speech. To improve speech enhancement performance, we tackle the phase estimation problem in three ways. First, we propose Deep Complex U-Net, an advanced U-Net structured model incorporating well-defined complex-valued building blocks to deal with complex-valued spectrograms. Second, we propose a polar coordinate-wise complex-valued masking method to reflect the distribution of complex ideal ratio masks. Third, we define a novel loss function, weighted source-to-distortion ratio (wSDR) loss, which is designed to directly correlate with a quantitative evaluation measure. Our model was evaluated on a mixture of the Voice Bank corpus and DEMAND database, which has been widely used by many deep learning models for speech enhancement. Ablation experiments were conducted on the mixed dataset showing that all three proposed approaches are empirically valid. Experimental results show that the proposed method achieves state-of-the-art performance in all metrics, outperforming previous approaches by a large margin.

Neural separation of observed and unobserved distributions    

tl;dr An iterative neural method for extracting signals that are only observed mixed with other signals

Separating mixed distributions is a long standing challenge for machine learning and signal processing. Applications include: single-channel multi-speaker separation (cocktail party problem), singing voice separation and separating reflections from images. Most current methods either rely on making strong assumptions on the source distributions (e.g. sparsity, low rank, repetitiveness) or rely on having training samples of each source in the mixture. In this work, we tackle the scenario of extracting an unobserved distribution additively mixed with a signal from an observed (arbitrary) distribution. We introduce a new method: Neural Egg Separation - an iterative method that learns to separate the known distribution from progressively finer estimates of the unknown distribution. In some settings, Neural Egg Separation is initialization sensitive, we therefore introduce GLO Masking which ensures a good initialization. Extensive experiments show that our method outperforms current methods that use the same level of supervision and often achieves similar performance to full supervision.

On Generalization Bounds of a Family of Recurrent Neural Networks    

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Recurrent Neural Networks (RNNs) have been widely applied to sequential data analysis. Due to their complicated modeling structures, however, the theory behind is still largely missing. To connect theory and practice, we study the generalization properties of vanilla RNNs as well as their variants, including Minimal Gated Unit (MGU) and Long Short Term Memory (LSTM) RNNs. Specifically, our theory is established under the PAC-Learning framework. The generalization bound is presented in terms of the spectral norms of the weight matrices and the total number of parameters. We also establish refined generalization bounds with additional norm assumptions, and draw a comparison among these bounds. We remark: (1) Our generalization bound for vanilla RNNs is significantly tighter than the best of existing results; (2) We are not aware of any other generalization bounds for MGU and LSTM in the exiting literature; (3) We demonstrate the advantages of these variants in generalization.

A Unified View of Deep Metric Learning via Gradient Analysis    

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Loss functions play a pivotal role in deep metric learning (DML). A large variety of loss functions have been proposed in DML recently. However, it remains difficult to answer this question: what are the intrinsic differences among these loss functions?This paper answers this question by proposing a unified perspective to rethink deep metric loss functions. We show theoretically that most DML methods in deep metric learning, in view of gradient equivalence, are essentially weight assignment strategies of training pairs. Based on this unified view, we revisit several typical DML methods and disclose their hidden drawbacks. Moreover, we point out the key components of an effective DML approach which drives us to propose our weight assignment framework. We evaluate our method on image retrieval tasks, and show that it outperforms the state-of-the-art DML approaches by a significant margin on the CUB-200-2011, Cars-196, Stanford Online Products and In-Shop Clothes Retrieval datasets.

In search of theoretically grounded pruning    

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Deep learning relies on resource-heavy linear algebra operations which can be prohibitively expensive when deploying to constrained embedded and mobile devices, or even when training large-scale networks. One way to reduce a neural network's resource requirements is to sparsify its weight matrices - a process often referred to as pruning. It is typically achieved by removing least important weights as measured by some salience criterion, with pruning by magnitude being the most popular option. This, however, often makes close to random judgments. In this paper we aim to closely investigate the concept of model weight importance, with a particular focus on the magnitude criterion and its most suitable substitute. To this end we identify a suitable Statistical framework and derive deep model parameter asymptotic theory to use with it. Thus, we derive a statistically-grounded pruning criterion which we compare with the magnitude pruning both qualitatively and quantitatively. We find this criterion to better capture parameter salience, by accounting for its estimation uncertainty. This results in improved performance and easier post-pruned re-training.

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length    

tl;dr SGD is steered early on in training towards a region in which its step is too large compared to curvature, which impacts the rest of training.

Training of deep neural networks with Stochastic Gradient Descent (SGD) typically ends in regions of the weight space, where both the generalization properties and the flatness of the local loss curvature depend on the learning rate and the batch size. We discover that a related phenomena happens in the early phase of training and study its consequences. Initially, SGD visits increasingly sharp regions of the loss surface, reaching a maximum sharpness determined by both the learning rate and the batch-size of SGD. At this early peak value, an SGD step is on average too large to minimize the loss along the directions corresponding to the largest eigenvalues of the Hessian (i.e. the sharpest directions). To query the importance of this phenomena for training, we study a variant of SGD using a reduced learning rate along the sharpest directions and show that it can improve training speed while finding both sharper and better--generalizing solution, compared to vanilla SGD. Overall, our results show that the SGD dynamics along the sharpest directions influence the regions of the weight space visited, the overall training speed, and generalization ability.

Transferrable End-to-End Learning for Protein Interface Prediction    

tl;dr We demonstrate the first successful application of transfer learning to atomic-level data in order to build a state-of-the-art end-to-end learning model for the protein interface prediction problem.

While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question. In this work we demonstrate that representations learned from raw atomic coordinates can outperform hand-engineered structural features while displaying a much higher degree of transferrability. To do so, we focus on a central problem in biology: predicting how proteins interact with one another—that is, which surfaces of one protein bind to which surfaces of another protein. We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction. Despite using only spatial coordinates and identities of atoms as inputs, SASNet outperforms state-of-the-art methods that rely on hand-engineered, high-level features. These results are particularly striking because we train the method entirely on a significantly biased data set that does not account for the fact that proteins deform when binding to one another. Demonstrating the first successful application of transfer learning to atomic-level data, our network maintains high performance, without retraining, when tested on real cases in which proteins do deform.


tl;dr We introduce a new PixelCNN-based auto-regressive generation approach that enhances the generation time by skimming the pixels.

Pixel convolutional neural network (PixelCNN) has provided promising results in image generation. However, it requires heavy computation time for inference, which deters its use in practice. Here, we propose a new generation method based on PixelCNN, dubbed Skim-PixelCNN that remarkably reduces inference time by skimming easy pixels. On top of a vanilla PixelCNN, we introduce two main components: an efficient generator that generates a set of next pixels in one shot and a confidence estimator that measures the confidence of the generated pixels. Based on the confidence, our model decides whether it skims or redraw the pixel using the vanilla PixelCNN. From the quantitative and qualitative experiments on diverse public image datasets, we show that our method can significantly reduce the computational overhead while its generation performance is comparable to or even improved that of the vanilla PixelCNN.

Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets    

tl;dr We make the first theoretical justification for the concept of straight-through estimator.

Training activation quantized neural networks involves piecewise constant loss functions with the sampled gradient vanishing almost everywhere, which is undesirable for back-propagation. An empirical way around this issue is to use a straight-through estimator (STE) (Bengio et al., 2013) in the backward pass, so that the resulting unusual “gradient” becomes non-trivial. In this paper, we make the first theoretical justification for the concept of STE, by considering the problem of learning a one-hidden-layer convolutional network with binarized ReLU activation and Gaussian input data. We refer to the unusual “gradient” based on STE as coarse gradient, which essentially is not the gradient of any function. Apparently, the choice of STE is not unique. We prove that if the STE is properly chosen, the negative expected coarse gradient is a descent direction for minimizing the population loss, and the associated coarse gradient descent algorithm converges to a local minimum (more rigorously, a critical point) of the population loss minimization problem. Moreover, we show that a relatively poor choice of STE may lead to instability of the training algorithm near certain local minima, which is also validated by our CIFAR-10 experiments.

Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition    

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Deep learning for object classification relies heavily on convolutional models. While effective, CNNs are rarely interpretable after the fact. An attention mechanism can be used to highlight the area of the image that the model focuses on thus offering a narrow view into the mechanism of classification. We expand on this idea by forcing the method to explicitly align images to be classified to reference images representing the classes. The mechanism of alignment is learned and therefore does not require that the reference objects are anything like those being classified. Beyond explanation, our exemplar based cross-alignment method enables classification with only a single example per category (one-shot). Our model cuts the 5-way, 1-shot error rate in Omniglot from 2.1\% to 1.4\% and in MiniImageNet from 53.5\% to 46.5\% while simultaneously providing point-wise alignment information providing some understanding on what the network is capturing. This method of alignment also enables the recognition of an unsupported class (open-set) in the one-shot setting while maintaining an F1-score of above 0.5 for Omniglot even with 19 other distracting classes while baselines completely fail to separate the open-set class in the one-shot setting.


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We propose Distributional Concavity (DC) regularization for GANs, a functional gradient-based method that promotes the entropy of the generator distribution and works against mode collapse. Our DC regularization is an easy-to-implement method that can be used in combination with the current state of the art methods like Spectral Normalization and WGAN-GP to further improve the performance. We will not only show that our DC regularization can achieve highly competi- tive results on ImageNet and CIFAR datasets in terms of Inception score and FID score, but also provide a mathematical guarantee that our method can always in- crease the entropy of the generator distribution. We will also show an intimate theoretical connection between our method and the theory of optimal transport.

Faster Training by Selecting Samples Using Embeddings    

tl;dr Training is sped up by using a dataset that has been subsampled through embedding analysis.

Long training times have increasingly become a burden for researchers by slowing down the pace of innovation, with some models taking days or weeks to train. In this paper, a new, general technique is presented that aims to speed up the training process by using a thinned-down training dataset. By leveraging autoencoders and the unique properties of embedding spaces, we are able to filter training datasets to include only those samples that matter the most. Through evaluation on a standard CIFAR-10 image classification task, this technique is shown to be effective. With this technique, training times can be reduced with a minimal loss in accuracy. Conversely, given a fixed training time budget, the technique was shown to improve accuracy by over 50%. This technique is a practical tool for achieving better results with large datasets and limited computational budgets.

Selectivity metrics can overestimate the selectivity of units: a case study on AlexNet    

tl;dr Common selectivity metrics overestimate the selectivity of units, true object detectors and localist codes are extremely rare, but class selectivity does increase with depth.

Various methods of measuring unit selectivity have been developed in order to understand the representations learned by neural networks. Here we undertake a comparison of four such measures on the well studied network AlexNet. In contrast to work on recurrent neural networks (RNNs), we fail to find any 100\% selective `localist units' in the hidden layers of AlexNet, and demonstrate that previous assessments of selectivity suggest a higher level of selectivity than is warranted, with the most selective units only responding most strongly to a small minority of images from within a category. No difference in selectivity was found between layers \layer{fc6} and \layer{fc7}, and \layer{fc8} was much more selective. Only the output \layer{prob} layer contained any localist units. We also generated images that maximally activated individual units and found that under (5\%) of units in \layer{fc6} and \layer{conv5} produced images of interpretable objects that humans consistently labeled, whereas \layer{fc8} produced over 50\% interpretable images. We consider why different degrees of selectivity are observed with RNNs and AlexNet, and suggest visualizing activations with jitterplots, aside from being comparable to neuroscience techniques, are a good first step to assessing unit selectivity.

Multi-Domain Adversarial Learning    

tl;dr Adversarial Domain adaptation and Multi-domain learning: a new loss to handle multi- and single-domain classes in the semi-supervised setting.

Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias. This paper presents a multi-domain adversarial learning approach, MuLANN, to leverage multiple datasets with overlapping but distinct class sets, in a semi-supervised setting. Our contributions include: i) a bound on the average- and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss to accommodate semi-supervised multi-domain learning and domain adaptation; iii) the experimental validation of the approach, improving on the state-of-the-art on two standard image benchmarks, and a novel bioimage dataset, Cell.


tl;dr We present a general technique toward 8-bit low precision inference of convolutional neural networks.

High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications. This paper presents a general technique toward 8-bit low precision inference of convolutional neural networks, including 1) channel-wise scale factors of weights, especially for depthwise convolution, 2) Winograd convolution, and 3) topology-wise 8-bit support. We experiment the techniques on top of a widely-used deep learning framework. The 8-bit optimized model is automatically generated with a calibration process from FP32 model without the need of fine-tuning or retraining. We perform a systematical and comprehensive study on 18 widely-used convolutional neural networks and demonstrate the effectiveness of 8-bit low precision inference across a wide range of applications and use cases, including image classification, object detection, image segmentation, and super resolution. We show that the inference throughput and latency are improved by 1.6X and 1.5X respectively with minimal within 0.6%1to no loss in accuracy from FP32 baseline. We believe the methodology can provide the guidance and reference design of 8-bit low precision inference for other frameworks. All the code and models will be publicly available soon.

Learning Graph Representations by Dendrograms    

tl;dr Novel quality metric for hierarchical graph clustering

Hierarchical clustering is a common approach to analysing the multi-scale structure of graphs observed in practice. We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram encoding the hierarchy. The best representation of the graph for this metric in turn yields a novel hierarchical clustering algorithm. Experiments on both real and synthetic data illustrate the efficiency of the approach.

Pixel Chem: A Representation for Predicting Material Properties with Neural Network    

tl;dr Proposed a unified, physics based representation of material structures to predict various properties with neural netwoek.

In this work we developed a new representation of the chemical information for the machine learning models, with benefits from both the real space (R-space) and energy space (K-space). Different from the previous symmetric matrix presentations, the charge transfer channel based on Pauling’s electronegativity is derived from the dependence on real space distance and orbitals for the hetero atomic structures. This representation can work for the bulk materials as well as the low dimensional nano materials, and can map the R-space and K-space into the pixel space (P-space) by training and testing 130k structures. P-space can well reproduce the R-space quantities within error 0.53. This new asymmetric matrix representation double the information storage than the previous symmetric representations.This work provides a new dimension for the computational chemistry towards the machine learning architecture.

CoT: Cooperative Training for Generative Modeling of Discrete Data    

tl;dr We proposed Cooperative Training, a novel training algorithm for generative modeling of discrete data.

We propose Cooperative Training (CoT) for training generative models that measure a tractable density for discrete data. CoT coordinately trains a generator G and an auxiliary predictive mediator M. The training target of M is to estimate a mixture density of the learned distribution G and the target distribution P, and that of G is to minimize the Jensen-Shannon divergence estimated through M. CoT achieves independent success without the necessity of pre-training via Maximum Likelihood Estimation or involving high-variance algorithms like REINFORCE. This low-variance algorithm is theoretically proved to be superior for both sample generation and likelihood prediction. We also theoretically and empirically show the superiority of CoT over most previous algorithms in terms of generative quality and diversity, predictive generalization ability and computational cost.

Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae    

tl;dr We propose to use explicit vector algebraic formulae projection as an alternative way to visualize embedding spaces specifically tailored for task-oriented analysis tasks and it outperforms t-SNE in our user study.

Embeddings are a fundamental component of many modern machine learning and natural language processing models. Understanding them and visualizing them is essential for gathering insights about the information they capture and the behavior of the models. State of the art in analyzing embeddings consists in projecting them in two-dimensional planes without any interpretable semantics associated to the axes of the projection, which makes detailed analyses and comparison among multiple sets of embeddings challenging. In this work, we propose to use explicit axes defined as algebraic formulae over embeddings to project them into a lower dimensional, but semantically meaningful subspace, as a simple yet effective analysis and visualization methodology. This methodology assigns an interpretable semantics to the measures of variability and the axes of visualizations, allowing for both comparisons among different sets of embeddings and fine-grained inspection of the embedding spaces. We demonstrate the power of the proposed methodology through a series of case studies that make use of visualizations constructed around the underlying methodology and through a user study. The results show how the methodology is effective at providing more profound insights than classical projection methods and how it is widely applicable to many other use cases.

Diminishing Batch Normalization    

tl;dr We propose a extension of the batch normalization, show a first-of-its-kind convergence analysis for this extension and show in numerical experiments that it has better performance than the original batch normalizatin.

In this paper, we propose a generalization of the BN algorithm, diminishing batch normalization (DBN), where we update the BN parameters in a diminishing moving average way. Batch normalization (BN) is very effective in accelerating the convergence of a neural network training phase that it has become a common practice. Our proposed DBN algorithm remains the overall structure of the original BN algorithm while introduces a weighted averaging update to some trainable parameters. We provide an analysis of the convergence of the DBN algorithm that converges to a stationary point with respect to trainable parameters. Our analysis can be easily generalized for original BN algorithm by setting some parameters to constant. To the best knowledge of authors, this analysis is the first of its kind for convergence with Batch Normalization introduced. We analyze a two-layer model with arbitrary activation function. The primary challenge of the analysis is the fact that some parameters are updated by gradient while others are not. The convergence analysis applies to any activation function that satisfies our common assumptions. For the analysis, we also show the sufficient and necessary conditions for the stepsizes and diminishing weights to ensure the convergence. In the numerical experiments, we use more complex models with more layers and ReLU activation. We observe that DBN outperforms the original BN algorithm on Imagenet, MNIST, NI and CIFAR-10 datasets with reasonable complex FNN and CNN models.

Towards Resisting Large Data Variations via Introspective Learning    

tl;dr We propose a principled approach that endows classifiers with the ability to resist larger variations between training and testing data in an intelligent and efficient manner.

Learning deep networks which can resist large variations between training andtesting data is essential to build accurate and robust image classifiers. Towardsthis end, a typical strategy is to apply data augmentation to enlarge the trainingset. However, standard data augmentation is essentially a brute-force strategywhich is inefficient, as it performs all the pre-defined transformations to everytraining sample. In this paper, we propose a principled approach to train networkswith significantly improved resistance to large variations between training andtesting data. This is achieved by embedding a learnable transformation moduleinto the introspective networks (Jin et al., 2017; Lazarow et al., 2017; Lee et al.,2018), which is a convolutional neural network (CNN) classifier empowered withgenerative capabilities. Our approach alternatively synthesizes pseudo-negativesamples with learned transformations and enhances the classifier by retraining itwith synthesized samples. Experimental results verify that our approach signif-icantly improves the ability of deep networks to resist large variations betweentraining and testing data and achieves classification accuracy improvements onseveral benchmark datasets, including MNIST, affNIST, SVHN and CIFAR-10.

TopicGAN: Unsupervised Text Generation from Explainable Latent Topics    

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Learning discrete representations of data and then generating data from the discovered representations have been increasingly studied, because the obtained discrete representations can benefit unsupervised learning. However, the performance of learning discrete representations of textual data with deep generative models has not been widely explored. In this work, we propose TopicGAN, a two-step generative model on text generation, which is able to discover discrete latent topics of texts and generate natural language from the discovered latent topics in an unsupervised fashion. Promising results are shown on unsupervised text classification and text generation for both subjective and objective evaluation.

A Mean Field Theory of Batch Normalization    

tl;dr Batch normalization causes exploding gradients in vanilla feedforward networks.

We develop a mean field theory for batch normalization in fully-connected feedforward neural networks. In so doing, we provide a precise characterization of signal propagation and gradient backpropagation in wide batch-normalized networks at initialization. We find that gradient signals grow exponentially in depth and that these exploding gradients cannot be eliminated by tuning the initial weight variances or by adjusting the nonlinear activation function. Indeed, batch normalization itself is the cause of gradient explosion. As a result, vanilla batch-normalized networks without skip connections are not trainable at large depths for common initialization schemes, a prediction that we verify with a variety of empirical simulations. While gradient explosion cannot be eliminated, it can be reduced by tuning the network close to the linear regime, which improves the trainability of deep batch-normalized networks without residual connections. Finally, we investigate the learning dynamics of batch-normalized networks and observe that after a single step of optimization the networks achieve a relatively stable equilibrium in which gradients have dramatically smaller dynamic range.

Distribution-Interpolation Trade off in Generative Models    

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We investigate the properties of multidimensional probability distributions in the context of latent space prior distributions of implicit generative models. Our work revolves around the phenomena arising while decoding linear interpolations between two random latent vectors -- regions of latent space in close proximity to the origin of the space are oversampled, which restricts the usability of linear interpolations as a tool to analyse the latent space. We show that the distribution mismatch can be eliminated completely by a proper choice of the latent probability distribution or using non-linear interpolations. We prove that there is a trade off between the interpolation being linear, and the latent distribution having even the most basic properties required for stable training, such as finite mean. We use the multidimensional Cauchy distribution as an example of the prior distribution, and also provide a general method of creating non-linear interpolations, that is easily applicable to a large family of commonly used latent distributions.

An adaptive homeostatic algorithm for the unsupervised learning of visual features    

tl;dr Unsupervised learning is hard and depends on normalisation heuristics. Can we find another simpler approach?

The formation of structure in the brain, that is, of the connection between cells within neural populations, is by large an unsupervised learning process: The emergence of this architecture is mostly self-organized. In the primary visual cortex of mammals, for example, one may observe during development the formation of cells selective to localized, oriented features. This leads to the development of a rough representation of contours of the retinal image in area V1. We modeled these mechanisms using sparse Hebbian learning algorithms. These algorithms alternate a coding step to encode the information with a learning step to find the proper encoder. A major difficulty faced by these algorithms is to deduce a good representation while knowing immature encoders, and to learn good encoders with a non-optimal representation. To address this problem, we propose here to introduce a new regulation process between learning and coding, called homeostasis. Our homeostasis is compatible with a neuro-mimetic architecture and allows for the fast emergence of localized filters sensitive to orientation. The key to this algorithm lies in a simple adaptation mechanism based on non-linear functions that reconciles the antagonistic processes that occur at the coding and learning time scales. We tested this unsupervised algorithm with this homeostasis rule for a range of existing unsupervised learning algorithms coupled with different neural coding algorithms. In addition, we propose a simplification of this optimal homeostasis rule by implementing a simple heuristic on the probability of activation of neurons. Compared to the optimal homeostasis rule, we show that this heuristic allows to implement a more rapid unsupervised learning algorithm while keeping a large part of its effectiveness. These results demonstrate the potential application of such a strategy in machine learning and we illustrate this with one result in a convolutional neural network.


tl;dr We propose a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.

The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task. Yet, even with such meta-learning, the low-data problem in the novel classification task still remains. In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem. Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data. TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner. We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.

Learning Robust, Transferable Sentence Representations for Text Classification    

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Despite deep recurrent neural networks (RNNs) demonstrate strong performance in text classification, training RNN models are often expensive and requires an extensive collection of annotated data which may not be available. To overcome the data limitation issue, existing approaches leverage either pre-trained word embedding or sentence representation to lift the burden of training RNNs from scratch. In this paper, we show that jointly learning sentence representations from multiple text classification tasks and combining them with pre-trained word-level and sentence level encoders result in robust sentence representations that are useful for transfer learning. Extensive experiments and analyses using a wide range of transfer and linguistic tasks endorse the effectiveness of our approach.

A theoretical framework for deep locally connected ReLU network    

tl;dr This paper presents a theoretical framework that models data distribution explicitly for deep and locally connected ReLU network

Understanding theoretical properties of deep and locally connected nonlinear network, such as deep convolutional neural network (DCNN), is still a hard problem despite its empirical success. In this paper, we propose a novel theoretical framework for such networks with ReLU nonlinearity. The framework explicitly formulates data distribution, favors disentangled representations and is compatible with common regularization techniques such as Batch Norm. The framework is built upon teacher-student setting, by expanding the student forward/backward propagation onto the teacher's computational graph. The resulting model does not impose unrealistic assumptions (e.g., Gaussian inputs, independence of activation, etc). Our framework could help facilitate theoretical analysis of many practical issues, e.g. overfitting, generalization, disentangled representations in deep networks.

Siamese Capsule Networks    

tl;dr A variant of capsule networks that can be used for pairwise learning tasks. Results shows that Siamese Capsule Networks work well in the few shot learning setting.

Capsule Networks have shown encouraging results on defacto benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable. Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points. In doing so we introduce Siamese Capsule Networks, a new variant that can be used for pairwise learning tasks. The model is trained using contrastive loss with l2-normalized capsule encoded pose features. We find that Siamese Capsule Networks perform well against strong baselines on both pairwise learning datasets, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.

Manifold regularization with GANs for semi-supervised learning    

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Generative Adversarial Networks are powerful generative models that can model the manifold of natural images. We leverage this property to perform manifold regularization by approximating a variant of the Laplacian norm using a Monte Carlo approximation that is easily computed with the GAN. When incorporated into the semi-supervised feature-matching GAN we achieve state-of-the-art results for GAN-based semi-supervised learning on CIFAR-10 and SVHN benchmarks, with a method that is significantly easier to implement than competing methods. We find that manifold regularization improves the quality of generated images, and is affected by the quality of the GAN used to approximate the regularizer.

Improving the Generalization of Adversarial Training with Domain Adaptation    

tl;dr We propose a novel adversarial training with domain adaptation method that significantly improves the generalization ability on adversarial examples from different attacks.

By injecting adversarial examples into training data, the adversarial training method is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It may not provide sufficiently representative samples from the adversarial domain, leading to a weak generalization ability on adversarial examples from other attacks. To scale to large datasets, perturbations on inputs to generate adversarial examples are usually crafted using fast single-step attacks. This work is mainly focused on the adversarial training with the single-step yet efficient FGSM adversary. In this scenario, it is difficult to train a model with great generalization due to the lack of representative adversarial samples, aka the samples are unable to accurately reflect the adversarial domain. To address this problem, we propose a novel Adversarial Training with Domain Adaptation (ATDA) method by regarding the adversarial training with FGSM adversary as a domain adaption task with limited number of target domain samples. The main idea is to learn a representation that is semantically meaningful and domain invariant on the clean domain as well as the adversarial domain. Empirical evaluations demonstrate that ATDA can greatly improve the generalization of adversarial training and achieves state-of-the-art results on standard benchmark datasets.

Ada-Boundary: Accelerating the DNN Training via Adaptive Boundary Batch Selection    

tl;dr We suggest a smart batch selection technique called Ada-Boundary.

Neural networks can converge faster with help from a smarter batch selection strategy. In this regard, we propose Ada-Boundary, a novel adaptive-batch selection algorithm that constructs an effective mini-batch according to a learner’s level. Our key idea is to automatically derive the learner’s level using the decision boundary which evolves as the learning progresses. Thus, the samples near the current decision boundary are considered as the most effective to expedite convergence. Taking advantage of our design, Ada-Boundary maintains its dominance in various degrees of training difficulty. We demonstrate the advantage of Ada-Boundary by extensive experiments using two convolutional neural networks for three benchmark data sets. The experiment results show that Ada-Boundary improves the training time by up to 31.7% compared with the state-of-the-art strategy and by up to 33.5% compared with the baseline strategy.

Live Face De-Identification in Video    

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We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having the perception (pose, illumination and expression) fixed. We achieve this by a novel feed forward encoder-decoder network architecture that is conditioned on the high-level representation of a person's facial image. The network is global, in the sense that it does not need to be retrained for a given video or for a given identity, and it creates natural-looking image sequences with little distortion in time.

Generative Feature Matching Networks    

tl;dr A new non-adversarial feature matching-based approach to train generative models that achieves state-of-the-art results.

We propose a non-adversarial feature matching-based approach to train generative models. Our approach, Generative Feature Matching Networks (GFMN), leverages pretrained neural networks such as autoencoders and ConvNet classifiers to perform feature extraction. We perform an extensive number of experiments with different challenging datasets, including Imagenet. Our experimental results demonstrate that, due to the expressiveness of the features from pretrained Imagenet classifiers, even by just matching first order statistics, our approach can achieve state-of-the-art results for challenging benchmarks such as CIFAR10 and STL10.

Noise-Tempered Generative Adversarial Networks    

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We present a novel method to stabilize the training of generative adversarial networks. The training stability is often undermined by the limited and low-dimensional support of the probability density function of the data samples. To address this problem we propose to simultaneously train the generative adversarial networks against different additive noise models, including the noise-free case. The benefits of this approach are that: 1) The case with noise added to both real and generated samples extends the support of the probability density function of the data, while not compromising the exact matching of the original data distribution, and 2) The noise-free case allows the exact matching of the original data distribution. We demonstrate our approach with both fixed additive noise and with learned noise models. We show that our approach results in a stable and well-behaved training of even the original minimax GAN formulation. Moreover, our technique can be incorporated in most modern GAN formulations and leads to a consistent improvement on several common datasets.

Generalized Label Propagation Methods for Semi-Supervised Learning    

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The key challenge in semi-supervised learning is how to effectively leverage unlabeled data to improve learning performance. The classical label propagation method, despite its popularity, has limited modeling capability in that it only exploits graph information for making predictions. In this paper, we consider label propagation from a graph signal processing perspective and decompose it into three components: signal, filter, and classifier. By extending the three components, we propose a simple generalized label propagation (GLP) framework for semi-supervised learning. GLP naturally integrates graph and data feature information, and offers the flexibility of selecting appropriate filters and domain-specific classifiers for different applications. Interestingly, GLP also provides new insight into the popular graph convolutional network and elucidates its working mechanisms. Extensive experiments on three citation networks, one knowledge graph, and one image dataset demonstrate the efficiency and effectiveness of GLP.

Biologically-Plausible Learning Algorithms Can Scale to Large Datasets    

tl;dr Biologically plausible learning algorithms, particularly sign-symmetry, works well on ImageNet

The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this “weight transport problem” (Grossberg, 1987), two more biologically plausible algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP’s weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study by Bartunov et al. (2018) finds that although feedback alignment (FA) and some variants of target-propagation (TP) perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry algorithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights do not share magnitudes but share signs. We examine the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet, RetinaNet for MS COCO). Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks. These results complement the study by Bartunov et al. (2018), and establish a new benchmark for future biologically plausible learning algorithms on more difficult datasets and more complex architectures.

Small steps and giant leaps: Minimal Newton solvers for Deep Learning    

tl;dr minimal newton solver for deep learning

We propose a fast second-order method that can be used as a drop-in replacement for current deep learning solvers. Compared to stochastic gradient descent (SGD), it only requires two additional forward-mode automatic differentiation operations per iteration, which has a computational cost comparable to two standard forward passes and is easy to implement. Our method addresses long-standing issues with current second-order solvers, which invert an approximate Hessian matrix every iteration exactly or by conjugate-gradient methods, procedures that are much slower than a SGD step. Instead, we propose to keep a single estimate of the gradient projected by the inverse Hessian matrix, and update it once per iteration with just two passes over the network. This estimate has the same size and is similar to the momentum variable that is commonly used in SGD. No estimate of the Hessian is maintained. We first validate our method, called CurveBall, on small problems with known solutions (noisy Rosenbrock function and degenerate 2-layer linear networks), where current deep learning solvers struggle. We then train several large models on CIFAR and ImageNet, including ResNet and VGG-f networks, where we demonstrate faster convergence with no hyperparameter tuning. We also show our optimiser's generality by testing on a large set of randomly-generated architectures.

How Important is a Neuron    

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The problem of attributing a deep network’s prediction to its input/base features is well-studied (cf. Simonyan et al. (2013)). We introduce the notion of conductance to extend the notion of attribution to understanding the importance of hidden units. Informally, the conductance of a hidden unit of a deep network is the flow of attribution via this hidden unit. We can use conductance to understand the importance of a hidden unit to the prediction for a specific input, or over a set of inputs. We justify conductance in multiple ways via a qualitative comparison with other methods, via some axiomatic results, and via an empirical evaluation based on a feature selection task. The empirical evaluations are done using the Inception network over ImageNet data, and a convolutinal network over text data. In both cases, we demonstrate the effectiveness of conductance in identifying interesting insights about the internal workings of these networks.


tl;dr We propose a novel Bayesian network sparsification method that adaptively prunes networks according to inputs.

While variational dropout approaches have been shown to be effective for network sparsification, they are still suboptimal in the sense that they set the dropout rate for each neuron without consideration of the input data. With such input-independent dropout, each neuron is evolved to be generic across inputs, which makes it difficult to sparsify networks without accuracy loss. To overcome this limitation, we propose adaptive variational dropout whose probabilities are drawn from sparsity-inducing beta-Bernoulli prior. It allows each neuron to be evolved either to be generic or specific for certain inputs, or dropped altogether. Such input-adaptive sparsity-inducing dropout allows the resulting network to tolerate larger degree of sparsity without losing its expressive power by removing redundancies among features. We validate our dependent variational beta-Bernoulli dropout on multiple public datasets, on which it obtains significantly more compact networks than baseline methods, with consistent accuracy improvements over the base networks.


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Humans can robustly learn novel visual concepts even when images undergo various deformations and loose certain information. Incorporating this ability to synthesize deformed instances of new concepts might help visual recognition systems perform better one-shot learning, i.e., learning concepts from one or few examples. Our key insight is that, while the deformed images might not be visually realistic, they still maintain critical semantic information and contribute significantly in formulating classifier decision boundaries. Inspired by the recent progress on meta-learning, we combine a meta-learner with an image deformation network that produces additional training examples, and optimize both models in an endto- end manner. The deformation network learns to synthesize images by fusing a pair of images—a probe image that keeps the visual content and a gallery image that diversifies the deformations. We demonstrate results on the widely used oneshot learning benchmarks (miniImageNet and ImageNet 1K challenge datasets), which significantly outperform the previous state-of-the-art approaches.

Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion    

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This paper proposes a simple model for learning grid-like units for spatial awareness and navigation. In this model, the self-position of the agent is represented by a vector, and the self-motion of the agent is represented by a block-diagonal matrix. Each component of the vector is a unit (or a cell). The model consists of the following two sub-models. (1) Motion sub-model. The movement from the current position to the next position is modeled by matrix-vector multiplication, i.e., multiplying the matrix representation of the motion to the current vector representation of the position in order to obtain the vector representation of the next position. (2) Localization sub-model. The adjacency between any two positions is a monotone decreasing function of their Euclidean distance, and the adjacency is modeled by the inner product between the vector representations of the two positions. Both sub-models can be implemented by neural networks. The motion sub-model is a recurrent network with dynamic weight matrix, and the localization sub-model is a feedforward network. The model can be learned by minimizing a loss function that combines the loss functions of the two sub-models. The learned units exhibit grid-like patterns (as well as stripe patterns) in both 2D and 3D environments. The learned model can be used for path integral and path planning. Moreover, the learned representation is capable of error correction.

Polar Prototype Networks    

tl;dr This work proposes a class of networks that can jointly perform classification and regression by imposing layout structures in the network output space.

This paper proposes a neural network for classification and regression, without the need to learn layout structures in the output space. Standard solutions such as soft-max cross-entropy and mean squared error are effective but parametric, meaning that known inductive structures such as maximum margin separation and simplicity (Occam’s Razor) need to be learned for the task at hand. Instead, we propose polar prototype networks, a class of networks that explicitly states the structure, i.e. the layout, of the output. The structure is defined by polar prototypes, points on the hypersphere of the output space. For classification, each class is described by a single polar prototype and they are a priori distributed with maximal separation and equal shares on the hypersphere. Classes are assigned to prototypes randomly or based on semantic priors and training becomes a matter of minimizing angular distances between examples and their class prototypes. For regression, we show that training can be performed as a polar interpolation between two prototypes, arriving at a regression with higher-dimensional outputs. From empirical analysis, we find that polar prototype networks benefit from large margin separation and semantic class structure, while having a minimal description length in the output space. While the structure is simple, the performance is on par with (classification) or better than (regression) standard network methods. Moreover, we show that we gain the ability to perform regression and classification jointly in the same space, which is disentangled and interpretable by design.

Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions    

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Although stochastic gradient descent (SGD) method and its variants (e.g., stochastic momentum methods, AdaGrad) are algorithms of choice for solving non-convex problems (especially deep learning), big gaps still remain between the theory and the practice with many questions unresolved. For example, there is still a lack of theories of convergence for SGD and its variants that use stagewise step size and return an averaged solution in practice. In addition, theoretical insights of why adaptive step size of AdaGrad could improve non-adaptive step size of SGD is still missing for non-convex optimization. This paper aims to address these questions and fill the gap between theory and practice. We propose a universal stagewise optimization framework for a broad family of non-smooth non-convex problems with the following key features: (i) at each stage any suitable stochastic convex optimization algorithms (e.g., SGD or AdaGrad) that return an averaged solution can be employed for minimizing a regularized convex problem; (ii) the step size is decreased in a stagewise manner; (iii) an averaged solution is returned as the final solution. % that is selected from all stagewise averaged solutions with sampling probabilities increasing as the stage number. Our theoretical results of stagewise {\ada} exhibit its adaptive convergence, therefore shed insights on its faster convergence than stagewise SGD for problems with slowly growing cumulative stochastic gradients. To the best of our knowledge, these new results are the first of their kind for addressing the unresolved issues of existing theories mentioned earlier. Besides theoretical contributions, our empirical studies show that our stagewise variants of SGD, AdaGrad improve the generalization performance of existing variants/implementations of SGD and AdaGrad.


tl;dr case study on optimal deep learning model for UAVs

Over the passage of time Unmanned Autonomous Vehicles (UAVs), especially Autonomous flying drones grabbed a lot of attention in Artificial Intelligence. Since electronic technology is getting smaller, cheaper and more efficient, huge advancement in the study of UAVs has been observed recently. From monitoring floods, discerning the spread of algae in water bodies to detecting forest trail, their application is far and wide. Our work is mainly focused on autonomous flying drones where we establish a case study towards efficiency, robustness and accuracy of UAVs where we showed our results well supported through experiments. We provide details of the software and hardware architecture used in the study. We further discuss about our implementation algorithms and present experiments that provide a comparison between three different state-of-the-art algorithms namely TrailNet, InceptionResnet and MobileNet in terms of accuracy, robustness, power consumption and inference time. In our study, we have shown that MobileNet has produced better results with very less computational requirement and power consumption. We have also reported the challenges we have faced during our work as well as a brief discussion on our future work to improve safety features and performance.

Invariance and Inverse Stability under ReLU    

tl;dr We analyze the invertibility of deep neural networks by studying preimages of ReLU-layers and the stability of the inverse.

We flip the usual approach to study invariance and robustness of neural networks by considering the non-uniqueness and instability of the inverse mapping. We provide theoretical and numerical results on the inverse of ReLU-layers. First, we derive a necessary and sufficient condition on the existence of invariance that provides a geometric interpretation. Next, we move to robustness via analyzing local effects on the inverse. To conclude, we show how this reverse point of view not only provides insights into key effects, but also enables to view adversarial examples from different perspectives.


tl;dr We introduce an augmented robust feature space for streaming wifi data that is capable of tackling concept drift for indoor localization

We outline the problem of concept drifts for time series data. In this work, we analyze the temporal inconsistency of streaming wireless signals in the context of device-free passive indoor localization. We show that data obtained from WiFi channel state information (CSI) can be used to train a robust system capable of performing room level localization. One of the most challenging issues for such a system is the movement of input data distribution to an unexplored space over time, which leads to an unwanted shift in the learned boundaries of the output space. In this work, we propose a phase and magnitude augmented feature space along with a standardization technique that is little affected by drifts. We show that this robust representation of the data yields better learning accuracy and requires less number of retraining.

G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space    

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It is well known that neural networks with rectified linear units (ReLU) activation functions are positively scale-invariant. Conventional algorithms like stochastic gradient descent optimize the neural networks in the vector space of weights, which is, however, not positively scale-invariant. This mismatch may lead to problems during the optimization process. Then, a natural question is: \emph{can we construct a new vector space that is positively scale-invariant and sufficient to represent ReLU neural networks so as to better facilitate the optimization process }? In this paper, we provide our positive answer to this question. First, we conduct a formal study on the positive scaling operators which forms a transformation group, denoted as $\mathcal{G}$. We prove that the value of a path (i.e. the product of the weights along the path) in the neural network is invariant to positive scaling and the value vector of all the paths is sufficient to represent the neural networks under mild conditions. Second, we show that one can identify some basis paths out of all the paths and prove that the linear span of their value vectors (denoted as $\mathcal{G}$-space) is an invariant space with lower dimension under the positive scaling group. Finally, we design stochastic gradient descent algorithm in $\mathcal{G}$-space (abbreviated as $\mathcal{G}$-SGD) to optimize the value vector of the basis paths of neural networks with little extra cost by leveraging back-propagation. Our experiments show that $\mathcal{G}$-SGD significantly outperforms the conventional SGD algorithm in optimizing ReLU networks on benchmark datasets.

Morph-Net: An Universal Function Approximator    

tl;dr Using mophological operation (dilation and erosion) we have defined a class of network which can approximate any continious function.

Artificial neural networks are built on the basic operation of linear combination and non-linear activation function. Theoretically this structure can approximate any continuous function with three layer architecture. But in practice learning the parameters of such network can be hard. Also the choice of activation function can greatly impact the performance of the network. In this paper we are proposing to replace the basic linear combination operation with non-linear operations that do away with the need of additional non-linear activation function. To this end we are proposing the use of elementary morphological operations (dilation and erosion) as the basic operation in neurons. We show that these networks (Denoted as Morph-Net) with morphological operations can approximate any smooth function requiring less number of parameters than what is necessary for normal neural networks. The results show that our network perform favorably when compared with similar structured network. We have carried out our experiments on MNIST, Fashion-MNIST, CIFAR10 and CIFAR100.

Computation-Efficient Quantization Method for Deep Neural Networks    

tl;dr A simple computation-efficient quantization training method for CNNs and RNNs.

Deep Neural Networks, being memory and computation intensive, are a challenge to deploy in smaller devices. Numerous quantization techniques have been proposed to reduce the inference latency/memory consumption. However, these techniques impose a large overhead on the training procedure or need to change the training process. We present a non-intrusive quantization technique based on re-training the full precision model, followed by directly optimizing the corresponding binary model. The quantization training process takes no longer than the original training process. We also propose a new loss function to regularize the weights, resulting in reduced quantization error. Combining both help us achieve full precision accuracy on CIFAR dataset using binary quantization. We also achieve full precision accuracy on WikiText-2 using 2 bit quantization. Comparable results are also shown for ImageNet. We also present a 1.5 bits hybrid model exceeding the performance of TWN LSTM model for WikiText-2.

From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference    

tl;dr Reformulate deep networks nonlinearities from a vector quantization scope and bridge most known nonlinearities together.

Nonlinearity is crucial to the performance of a deep (neural) network (DN). To date there has been little progress understanding the menagerie of available nonlinearities, but recently progress has been made on understanding the r\^{o}le played by piecewise affine and convex nonlinearities like the ReLU and absolute value activation functions and max-pooling. In particular, DN layers constructed from these operations can be interpreted as {\em max-affine spline operators} (MASOs) that have an elegant link to vector quantization (VQ) and $K$-means. While this is good theoretical progress, the entire MASO approach is predicated on the requirement that the nonlinearities be piecewise affine and convex, which precludes important activation functions like the sigmoid, hyperbolic tangent, and softmax. {\em This paper extends the MASO framework to these and an infinitely large class of new nonlinearities by linking deterministic MASOs with probabilistic Gaussian Mixture Models (GMMs).} We show that, under a GMM, piecewise affine, convex nonlinearities like ReLU, absolute value, and max-pooling can be interpreted as solutions to certain natural ``hard'' VQ inference problems, while sigmoid, hyperbolic tangent, and softmax can be interpreted as solutions to corresponding ``soft'' VQ inference problems. We further extend the framework by hybridizing the hard and soft VQ optimizations to create a $\beta$-VQ inference that interpolates between hard, soft, and linear VQ inference. A prime example of a $\beta$-VQ DN nonlinearity is the {\em swish} nonlinearity, which offers state-of-the-art performance in a range of computer vision tasks but was developed ad hoc by experimentation. Finally, we validate with experiments an important assertion of our theory, namely that DN performance can be significantly improved by enforcing orthogonality in its linear filters.

Aggregated Momentum: Stability Through Passive Damping    

tl;dr We introduce a simple variant of momentum optimization which is able to outperform classical momentum, Nesterov, and Adam on deep learning tasks with minimal hyperparameter tuning.

Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions. Its performance depends crucially on a damping coefficient. Largecamping coefficients can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typically resorts to small values such as 0.5 or 0.9. We propose Aggregated Momentum (AggMo), a variant of momentum which combines multiple velocity vectors with different damping coefficients. AggMo is trivial to implement, but significantly dampens oscillations, enabling it to remain stable even for aggressive damping coefficients such as 0.999. We reinterpret Nesterov's accelerated gradient descent as a special case of AggMo and analyze rates of convergence for quadratic objectives. Empirically, we find that AggMo is a suitable drop-in replacement for other momentum methods, and frequently delivers faster convergence with little to no tuning.


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Developing deep neural networks (DNNs) for manifold-valued data sets has gained much interest of late in the deep learning research community. Examples of manifold-valued data include data from omnidirectional cameras on automobiles, drones etc., diffusion magnetic resonance imaging, elastography and others. In this paper, we present a novel theoretical framework for DNNs to cope with manifold-valued data inputs. In doing this generalization, we draw parallels to the widely popular convolutional neural networks (CNNs). We call our network the ManifoldNet. As in vector spaces where convolutions are equivalent to computing the weighted mean of functions, an analogous definition for manifold-valued data can be constructed involving the computation of the weighted Fr\'{e}chet Mean (wFM). To this end, we present a provably convergent recursive computation of the wFM of the given data, where the weights makeup the convolution mask, to be learned. Further, we prove that the proposed wFM layer achieves a contraction mapping and hence the ManifoldNet does not need the additional non-linear ReLU unit used in standard CNNs. Operations such as pooling in traditional CNN are no longer necessary in this setting since wFM is already a pooling type operation. Analogous to the equivariance of convolution in Euclidean space to translations, we prove that the wFM is equivariant to the action of the group of isometries admitted by the Riemannian manifold on which the data reside. This equivariance property facilitates weight sharing within the network. We present experiments, using the ManifoldNet framework, to achieve video classification and image reconstruction using an auto-encoder+decoder setting. Experimental results demonstrate the efficacy of ManifoldNet in the context of classification and reconstruction accuracy.

Learning Neural Random Fields with Inclusive Auxiliary Generators    

tl;dr We develop a new approach to learning neural random fields and show that the new approach obtains state-of-the-art sample generation quality and achieves strong semi-supervised learning results on par with state-of-the-art deep generative models.

Neural random fields (NRFs), which are defined by using neural networks to implement potential functions in undirected models, provide an interesting family of model spaces for machine learning. In this paper we develop a new approach to learning NRFs with inclusive-divergence minimized auxiliary generator - the inclusive-NRF approach. The new approach enables us to flexibly use NRFs in unsupervised, supervised and semi-supervised settings and successfully train them in a black-box manner. Empirically, inclusive-NRFs achieve state-of-the-art sample generation quality on CIFAR-10 in both unsupervised and supervised settings. Semi-supervised inclusive-NRFs show strong classification results on par with state-of-the-art generative model based semi-supervised learning methods, and simultaneously achieve superior generation, on the widely benchmarked datasets - MNIST, SVHN and CIFAR-10.

Selective Self-Training for semi-supervised Learning    

tl;dr Our proposed algorithm does not use all of the unlabeled data for the training, and it rather uses them selectively.

Most of the conventional semi-supervised learning (SSL) methods assume that the classes of unlabeled data are contained in the set of classes of labeled data. In addition, these methods do not discriminate unlabeled samples and use all the unlabeled data for learning, which is not suitable for realistic situations. In this paper, we propose an SSL method called selective self-training (SST), which selectively decides whether to include each unlabeled sample in the training process or not. It is also designed to be applied to a more realistic situation where classes of unlabeled data are different from the ones of the labeled data. For the conventional SSL problems of fixed classes, the proposed method not only performs comparable to other conventional SSL algorithms, but also can be combined with other SSL algorithms. For the new SSL problems of increased classes where the conventional methods cannot be applied, the proposed method does not show any performance degradation even if the classes of unlabeled data are different from those of the labeled data.

COCO-GAN: Conditional Coordinate Generative Adversarial Network    

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Recent advancements on Generative Adversarial Network (GAN) have inspired a wide range of works that generate synthetic images. However, the current processes have to generate an entire image at once, and therefore resolutions are limited by memory or computational constraints. In this work, we propose COnditional COordinate GAN (COCO-GAN), which generates a specific patch of an image conditioned on a spatial position rather than the entire image at a time. The generated patches are later combined together to form a globally coherent full-image. With this process, we show that the generated image can achieve competitive quality to state-of-the-arts and the generated patches are locally smooth between consecutive neighbors. One direct implication of the COCO-GAN is that it can be applied onto any coordinate systems including the cylindrical systems which makes it feasible for generating panorama images. The fact that the patch generation process is independent to each other inspires a wide range of new applications: firstly, "Patch-Inspired Image Generation" enables us to generate the entire image based on a single patch. Secondly, "Partial-Scene Generation" allows us to generate images within a customized target region. Finally, thanks to COCO-GAN's patch generation and massive parallelism, which enables combining patches for generating a full-image with higher resolution than state-of-the-arts.

A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation    

tl;dr We use empirical tools of mode connectivity and SVCCA to investigate neural network training heuristics of learning rate restarts, warmup and knowledge distillation.

The convergence rate and final performance of common deep learning models have significantly benefited from recently proposed heuristics such as learning rate schedules, knowledge distillation, skip connections and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining the efficacy of these strategies can aid our understanding of deep learning landscapes and the training dynamics. Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations. Instead, we revisit the empirical analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz. mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons why the heuristics succeed. In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that: (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and (c) that the latent knowledge shared by the teacher is primarily disbursed in the deeper layers.

Learning From the Experience of Others: Approximate Empirical Bayes in Neural Networks    

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Learning deep neural networks could be understood as the combination of representation learning and learning halfspaces. While most previous work aims to diversify representation learning by data augmentations and regularizations, we explore the opposite direction through the lens of empirical Bayes method. Specifically, we propose a matrix-variate normal prior whose covariance matrix has a Kronecker product structure to capture the correlations in learning different neurons through backpropagation. The prior encourages neurons to learn from the experience of others, hence it provides an effective regularization when training large networks on small datasets. To optimize the model, we design an efficient block coordinate descent algorithm with analytic solutions. Empirically, we show that the proposed method helps the network converge to better local optima that also generalize better, and we verify the effectiveness of the approach on both multiclass classification and multitask regression problems with various network structures.

Adversarial Sampling for Active Learning    

tl;dr ASAL is a pool based active learning method that generates high entropy samples and retrieves matching samples from the pool in sub-linear time.

This paper proposes ASAL, a new pool based active learning method that generates high entropy samples. Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them for training. Hence, the quality of new samples is high and annotations are reliable. ASAL is particularly suitable for large data sets because it achieves a better run-time complexity (sub-linear) for sample selection than traditional uncertainty sampling (linear). We present a comprehensive set of experiments on two data sets and show that ASAL outperforms similar methods and clearly exceeds the established baseline (random sampling). In the discussion section we analyze in which situations ASAL performs best and why it is sometimes hard to outperform random sample selection. To the best of our knowledge this is the first adversarial active learning technique that is applied for multiple class problems using deep convolutional classifiers and demonstrates superior performance than random sample selection.


tl;dr we propose an algorithm of learning to prune network by enforcing structure sparsity penalties

This paper proposes a Pruning in Training (PiT) framework of learning to reduce the parameter size of networks. Different from existing works, our PiT framework employs the sparse penalties to train networks and thus help rank the importance of weights and filters. Our PiT algorithms can directly prune the network without any fine-tuning. The pruned networks can still achieve comparable performance to the original networks. In particular, we introduce the (Group) Lasso-type Penalty (L-P /GL-P), and (Group) Split LBI Penalty (S-P / GS-P) to regularize the networks, and a pruning strategy proposed is used in help prune the network. We conduct the extensive experiments on MNIST, Cifar-10, and miniImageNet. The results validate the efficacy of our proposed methods. Remarkably, on MNIST dataset, our PiT framework can save 17.5% parameter size of LeNet-5, which achieves the 98.47% recognition accuracy.

Shallow Learning For Deep Networks    

tl;dr We build CNNs layer by layer without end to end training and show that this kind of approach can scale to Imagenet, while having multiple favorable properties.

Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hiddenlayer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reportedas critical for success. We thus study CNNs on two large-scale image recognition tasks: ImageNet and CIFAR-10. Using a simple set of ideas for architecture and training we find that solving sequential 1-hidden-layer auxiliary problemsleads to a CNN that exceeds AlexNet performance on ImageNet. Extending ourtraining methodology to construct individual layers by solving 2-and-3-hiddenlayer auxiliary problems, we obtain an 11-layer network that exceeds VGG-11 on ImageNet obtaining 89.8% top-5 single crop. To our knowledge, this is the first competitive alternative to end-to-end training of CNNs that can scale to ImageNet. We conduct a wide range of experiments to study the properties this induces on the intermediate layers.

Collapse of deep and narrow neural nets    

tl;dr Deep and narrow neural networks will converge to erroneous mean or median states of the target function depending on the loss with high probability.

Recent theoretical work has demonstrated that deep neural networks have superior performance over shallow networks, but their training is more difficult, e.g., they suffer from the vanishing gradient problem. This problem can be typically resolved by the rectified linear unit (ReLU) activation. However, here we show that even for such activation, deep and narrow neural networks will converge to erroneous mean or median states of the target function depending on the loss with high probability. We demonstrate this collapse of deep and narrow neural networks both numerically and theoretically, and provide estimates of the probability of collapse. We also construct a diagram of a safe region of designing neural networks that avoid the collapse to erroneous states. Finally, we examine different ways of initialization and normalization that may avoid the collapse problem.

Connecting the Dots Between MLE and RL for Sequence Generation    

tl;dr A unified perspective of various learning algorithms for sequence generation, such as MLE, RL, RAML, data noising, etc.

Sequence generation models such as recurrent networks can be trained with a diverse set of learning algorithms. For example, maximum likelihood learning is simple and efficient, yet suffers from the exposure bias problem. Reinforcement learning like policy gradient addresses the problem but can have prohibitively poor exploration efficiency. A variety of other algorithms such as RAML, SPG, and data noising, have also been developed in different perspectives. This paper establishes a formal connection between these algorithms. We present a generalized entropy regularized policy optimization formulation, and show that the apparently divergent algorithms can all be reformulated as special instances of the framework, with the only difference being the configurations of reward function and a couple of hyperparameters. The unified interpretation offers a systematic view of the varying properties of exploration and learning efficiency. Besides, based on the framework, we present a new algorithm that dynamically interpolates among the existing algorithms for improved learning. Experiments on machine translation and text summarization demonstrate the superiority of the proposed algorithm.

Unsupervised Learning via Meta-Learning    

tl;dr An unsupervised learning method that uses meta-learning to enable efficient learning of downstream image classification tasks, outperforming state-of-the-art methods.

A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data. Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disentanglement, prediction, and other metrics. Instead, we develop an unsupervised learning method that explicitly optimizes for the ability to learn a variety of tasks from small amounts of data. To do so, we construct tasks from unlabeled data in an automatic way and run meta-learning over the constructed tasks. Surprisingly, we find that relatively simple mechanisms for task design, such as clustering unsupervised representations, lead to good performance on a variety of downstream tasks. Our experiments across four image datasets indicate that our unsupervised meta-learning approach acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks, improving upon the representation learned by four prior unsupervised learning methods.

Integrated Steganography and Steganalysis with Generative Adversarial Networks    

No tl;dr =[

Recently, generative adversarial network is the hotspot in research areas and industrial application areas. It's application on data generation in computer vision is most common usage. This paper extends its application to data hiding and security area. In this paper, we propose the novel framework to integrate steganography and steganalysis processes. The proposed framework applies generative adversarial networks as the core structure. The discriminative model simulate the steganalysis process, which can help us understand the sensitivity of cover images to semantic changes. The steganography generative model is to generate stego image which is aligned with the original cover image, and attempts to confuse steganalysis discriminative model. The introduction of cycle discriminative model and inconsistent loss can help to enhance the quality and security of generated stego image in the iterative training process. Training dataset is mixed with intact images as well as intentional attacked images. The mix training process can further improve the robustness and security of new framework. Through the qualitative, quantitative experiments and analysis, this novel framework shows compelling performance and advantages over the current state-of-the-art methods in steganography and steganalysis benchmarks.

TabNN: A Universal Neural Network Solution for Tabular Data    

tl;dr We propose a universal neural network solution to derive effective NN architectures for tabular data automatically.

Neural Network (NN) has achieved state-of-the-art performances in many tasks within image, speech, and text domains. Such great success is mainly due to special structure design to fit the particular data patterns, such as CNN capturing spatial locality and RNN modeling sequential dependency. Essentially, these specific NNs achieve good performance by leveraging the prior knowledge over corresponding domain data. Nevertheless, there are many applications with all kinds of tabular data in other domains. Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all. Without careful architecture design based on domain knowledge, it is quite challenging for NN to reach satisfactory performance in these tabular data domains. To fill the gap of NN in tabular data learning, we propose a universal neural network solution, called TabNN, to derive effective NN architectures for tabular data in all kinds of tasks automatically. Specifically, the design of TabNN follows two principles: \emph{to explicitly leverages expressive feature combinations} and \emph{to reduce model complexity}. Since GBDT has empirically proven its strength in modeling tabular data, we use GBDT to power the implementation of TabNN. Comprehensive experimental analysis on a variety of tabular datasets demonstrate that TabNN can achieve much better performance than many baseline solutions.

Gaussian-gated LSTM: Improved convergence by reducing state updates    

tl;dr Gaussian-gated LSTM is a novel time-gated LSTM RNN network that enables faster and better training on long sequence data.

Recurrent neural networks can be difficult to train on long sequence data due to the well-known vanishing gradient problem. Some architectures incorporate methods to reduce RNN state updates, therefore allowing the network to preserve memory over long temporal intervals. To address these problems of convergence, this paper proposes a timing-gated LSTM RNN model, called the Gaussian-gated LSTM (g-LSTM). The time gate controls when a neuron can be updated during training, enabling longer memory persistence and better error-gradient flow. This model captures long-temporal dependencies better than an LSTM and the time gate parameters can be learned even from non-optimal initialization values. Because the time gate limits the updates of the neuron state, the number of computes needed for the network update is also reduced. By adding a computational budget term to the training loss, we can obtain a network which further reduces the number of computes by at least 10x. Finally, by employing a temporal curriculum learning schedule for the g-LSTM, we can reduce the convergence time of the equivalent LSTM network on long sequences.

Riemannian Adaptive Optimization Methods    

tl;dr Adapting Adam, Amsgrad, Adagrad to Riemannian manifolds.

Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings. However, some of the most popular of these optimization tools - namely Adam , Adagrad and the more recent Amsgrad - remain to be generalized to Riemannian manifolds. We discuss the difficulty of generalizing such adaptive schemes to the most agnostic Riemannian setting, and then provide algorithms and convergence proofs for geodesically convex objectives in the particular case of a product of Riemannian manifolds, in which adaptivity is implemented across manifolds in the cartesian product. Our generalization is tight in the sense that choosing the Euclidean space as Riemannian manifold yields the same algorithms and regret bounds as those that were already known for the standard algorithms. Experimentally, we show faster convergence and to a lower train loss value for Riemannian adaptive methods over their corresponding baselines on the realistic task of embedding the WordNet taxonomy in the Poincare ball.

Learning a Neural-network-based Representation for Open Set Recognition    

tl;dr In this paper, we present a neural network based representation for addressing the open set recognition problem.

In this paper, we present a neural network based representation for addressing the open set recognition problem. In this representation instances from the same class are close to each other while instances from different classes are further apart, resulting in statistically significant improvement when compared to other approaches on three datasets from two different domains.

Learning to Search Efficient DenseNet with Layer-wise Pruning    

No tl;dr =[

Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources. The DenseNet, one of the recently proposed neural network architecture, has achieved the state-of-the-art performance in many visual tasks. However, it has great redundancy due to the dense connections of the internal structure, which leads to high computational costs in training such dense networks. To address this issue, we design a reinforcement learning framework to search for efficient DenseNet architectures with layer-wise pruning (LWP) for different tasks, while retaining the original advantages of DenseNet, such as feature reuse, short paths, etc. In this framework, an agent evaluates the importance of each connection between any two block layers, and prunes the redundant connections. In addition, a novel reward-shaping trick is introduced to make DenseNet reach a better trade-off between accuracy and float point operations (FLOPs). Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives.

Neural Collobrative Networks    

No tl;dr =[

This paper presents a conceptually general and modularized neural collaborative network (NCN), which overcomes the limitations of the traditional convolutional neural networks (CNNs) in several aspects. Firstly, our NCN can directly handle non-Euclidean data without any pre-processing (e.g., graph normalizations) by defining a simple yet basic unit named neuron array for feature representation. Secondly, our NCN is capable of achieving both rotational equivariance and invariance properties via a simple yet powerful neuron collaboration mechanism, which imposes a ``glocal'' operation to capture both global and local information among neuron arrays within each layer. Thirdly, compared to the state-of-the-art networks that using large CNN kernels, our NCN with considerably fewer parameters can also achieve their strengths in feature learning by only exploiting highly efficient 1x1 convolution operations. Extensive experimental analyses on learning feature representation, handling novel viewpoints, and handling non-euclidean data demonstrate that our NCN can not only achieve state-of-the-art performance but also overcome the limitation of the conventional CNNs. The source codes will be released to facilite future researches after the review period for ensuring the anonymity.

Equi-normalization of Neural Networks    

tl;dr Fast iterative algorithm to balance the energy of a network while staying in the same functional equivalence class

Modern neural networks are over-parametrized. In particular, each rectified linear hidden unit can be modified by a multiplicative factor by adjusting input and out- put weights, without changing the rest of the network. Inspired by the Sinkhorn-Knopp algorithm, we introduce a fast iterative method for minimizing the l2 norm of the weights, equivalently the weight decay regularizer. It provably converges to a unique solution. Interleaving our algorithm with SGD during training improves the test accuracy. For small batches, our approach offers an alternative to batch- and group- normalization on CIFAR-10 and ImageNet with a ResNet-18.

Hint-based Training for Non-Autoregressive Translation    

tl;dr We develop a training algorithm for non-autoregressive machine translation models, achieving comparable accuracy to strong autoregressive baselines, but one order of magnitude faster in inference.

Machine translation is an important real-world application, and neural network-based AutoRegressive Translation (ART) models have achieved very promising accuracy. Due to the unparallelizable nature of the autoregressive factorization, ART models have to generate tokens one by one during decoding and thus suffer from high inference latency. Recently, Non-AutoRegressive Translation (NART) models were proposed to reduce the inference time. However, they could only achieve inferior accuracy compared with ART models. To improve the accuracy of NART models, in this paper, we propose to leverage the hints from a well-trained ART model to train the NART model. We define two hints for the machine translation task: hints from hidden states and hints from word alignments, and use such hints to regularize the optimization of NART models. Experimental results show that the NART model trained with hints could achieve significantly better translation performance than previous NART models on several tasks. In particular, for the WMT14 En-De and De-En task, we obtain BLEU scores of 25.20 and 29.52 respectively, which largely outperforms the previous non-autoregressive baselines. It is even comparable to a strong LSTM-based ART model (24.60 on WMT14 En-De), but one order of magnitude faster in inference.

Filter Training and Maximum Response: Classification via Discerning    

tl;dr The proposed scheme mimics the classification process mediated by a series of one component picking.

This report introduces a training and recognition scheme, in which classification is realized via class-wise discerning. Trained with datasets whose labels are randomly shuffled except for one class of interest, a neural network learns class-wise parameter values, and remolds itself from a feature sorter into feature filters, each of which discerns objects belonging to one of the classes only. Classification of an input can be inferred from the maximum response of the filters. A multiple check with multiple versions of filters can diminish fluctuation and yields better performance. This scheme of discerning, maximum response and multiple check is a method of general viability to improve performance of feedforward networks, and the filter training itself is a promising feature abstraction procedure. In contrast to the direct sorting, the scheme mimics the classification process mediated by a series of one component picking.

Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout    

tl;dr Realizing the drawbacks when applying original dropout on DenseNet, we craft the design of dropout method from three aspects, the idea of which could also be applied on other CNN models.

Recently convolutional neural networks (CNNs) achieve great accuracy in visual recognition tasks. DenseNet becomes one of the most popular CNN models due to its effectiveness in feature-reuse. However, like other CNN models, DenseNets also face overfitting problem if not severer. Existing dropout method can be applied but not as effective due to the introduced nonlinear connections. In particular, the property of feature-reuse in DenseNet will be impeded, and the dropout effect will be weakened by the spatial correlation inside feature maps. To address these problems, we craft the design of a specialized dropout method from three aspects, dropout location, dropout granularity, and dropout probability. The insights attained here could potentially be applied as a general approach for boosting the accuracy of other CNN models with similar nonlinear connections. Experimental results show that DenseNets with our specialized dropout method yield better accuracy compared to vanilla DenseNet and state-of-the-art CNN models, and such accuracy boost increases with the model depth.

On the Spectral Bias of Neural Networks    

tl;dr We investigate ReLU networks in the Fourier domain and demonstrate peculiar behaviour.

Prior work has theoretically established neural networks as a class of highly expressive functions. Their ability to memorize even random input-output mapping with 100% accuracy can be seen as a practical implication of this aspect. In this work we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis to study neural networks, We show that deep ReLU networks are biased towards low frequency functions, meaning that, they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects this spectral bias by showing strong evidence that different manifold shapes induce significantly different learning curves for deep ReLU networks and present a theoretical understanding of this behavior. Finally, we study the robustness of parameters to develop the intuition that parameters of a network must work together to express high frequency functions.

Jumpout: Improved Dropout for Deep Neural Networks with Rectified Linear Units    

tl;dr Jumpout applies three simple yet effective modifications to dropout, based on novel understandings about the generalization performance of DNN with ReLU in local regions.

Dropout is a simple yet effective technique to improve the generalization performance and prevent overfitting in deep neural networks (DNNs). In this paper, we discuss three novel observations about dropout to better understand the generalization of DNNs with rectified linear unit (ReLU) activations: 1) dropout is a smoothing technique that encourages each local linear model of a DNN to be trained on data points from nearby regions; 2) a constant dropout rate can result in effective neural-deactivation rates that are significantly different for layers with different fractions of activated neurons; and 3) the rescaling factor of dropout causes an inconsistency to occur between the normalization during training and testing conditions when batch normalization is also used. The above leads to three simple but nontrivial improvements to dropout resulting in our proposed method "Jumpout." Jumpout samples the dropout rate using a monotone decreasing distribution (such as the right part of a truncated Gaussian), so the local linear model at each data point is trained, with high probability, to work better for data points from nearby than from more distant regions. Instead of tuning a dropout rate for each layer and applying it to all samples, jumpout moreover adaptively normalizes the dropout rate at each layer and every training sample/batch, so the effective dropout rate applied to the activated neurons are kept the same. Moreover, we rescale the outputs of jumpout for a better trade-off that keeps both the variance and mean of neurons more consistent between training and test phases, which mitigates the incompatibility between dropout and batch normalization. Compared to the original dropout, jumpout shows significantly improved performance on CIFAR10, CIFAR100, Fashion- MNIST, STL10, SVHN, ImageNet-1k, etc., while introducing negligible additional memory and computation costs.

ChainGAN: A sequential approach to GANs    

tl;dr Multistep generation process for GANs

We propose a new architecture and training methodology for generative adversarial networks. Current approaches attempt to learn the transformation from a noise sample to a generated data sample in one shot. Our proposed generator architecture, called ChainGAN, uses a two-step process. It first attempts to transform a noise vector into a crude sample, similar to a traditional generator. Next, a chain of networks, called editors, attempt to sequentially enhance this sample. We train each of these units independently, instead of with end-to-end backpropagation on the entire chain. Our model is robust, efficient, and flexible as we can apply it to various network architectures. We provide rationale for our choices and experimentally evaluate our model, achieving competitive results on several datasets.

An Exhaustive Analysis of Lazy vs. Eager Learning Methods for Real-Estate Property Investment    

No tl;dr =[

Accurate rent prediction in real estate investment can help in generating capital gains and guaranty a financial success. In this paper, we carry out a comprehensive analysis and study of eleven machine learning algorithms for rent prediction, including Linear Regression, Multilayer Perceptron, Random Forest, KNN, ML-KNN, Locally Weighted Learning, SMO, SVM, J48, lazy Decision Tree (i.e., lazy DT), and KStar algorithms. Our contribution in this paper is twofold: (1) We present a comprehensive analysis of internal and external attributes of a real-estate housing dataset and their correlation with rental prices. (2) We use rental prediction as a platform to study and compare the performance of eager vs. lazy machine learning methods using myriad of ML algorithms. We train our rent prediction models using a Zillow data set of 4K real estate properties in Virginia State of the US, including three house types of single-family, townhouse, and condo. Each data instance in the dataset has 21 internal attributes (e.g., area space, price, number of bed/bath, rent, school rating, so forth). In addition to Zillow data, external attributes like walk/transit score, and crime rate are collected from online data sources. A subset of the collected features - determined by the PCA technique- are selected to tune the parameters of the prediction models. We employ a hierarchical clustering approach to cluster the data based on two factors of house type, and average rent estimate of zip codes. We evaluate and compare the efficacy of the tuned prediction models based on two metrics of R-squared and Mean Absolute Error, applied on unseen data. Based on our study, lazy models like KStar lead to higher accuracy and lower prediction error compared to eager methods like J48 and LR. However, it is not necessarily found to be an overarching conclusion drawn from the comparison between all the lazy and eager methods in this work.

A bird's eye view on coherence, and a worm's eye view on cohesion    

tl;dr We encode linguistic properties, such as, coherence and cohesion, into expert discriminators and improve text generation.

Generating coherent and cohesive long-form texts is a challenging problem in natural language generation. Previous works relied on a large amount of human-generated texts to train language models, however, few attempted to explicitly model the desired linguistic properties of natural language text, such as coherence and cohesion. In this work, we train two expert discriminators for coherence and cohesion, respectively, to provide hierarchical feedback for text generation. We also propose a simple variant of policy gradient, called 'negative-critical sequence training', using margin rewards, in which the 'baseline' is constructed from randomly generated negative samples. We demonstrate the effectiveness of our approach through empirical studies, showing significant improvements over the strong baseline -- attention-based bidirectional MLE-trained neural language model -- in a number of automated metrics. The proposed discriminators can serve as baseline architectures to promote further research to better extract, encode, and transfer essential qualities from texts.

Lipschitz regularized Deep Neural Networks converge and generalize    

tl;dr We prove generalization of DNNs by adding a Lipschitz regularization term to the training loss

Generalization of deep neural networks (DNNs) is an open problem which, if solved, could impact the reliability and verification of deep neural network architectures. In this paper, we show that if the usual fidelity term used in training DNNs is augmented by a Lipschitz regularization term, then the networks converge and generalize. The convergence is in the limit as the number of data points, $n\to \infty$, while also allowing the network to grow as needed to fit the data. Two regimes are identified: in the case of clean labels, we prove convergence to the label function which corresponds to zero loss, in the case of corrupted labels which we prove convergence to a regularized label function which is the solution of a limiting variational problem. In both cases, a convergence rate is also provided.

Wizard of Wikipedia: Knowledge-Powered Conversational Agents    

tl;dr We build knowledgeable conversational agents by conditioning on Wikipedia + a new supervised task.

In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically “generate and hope” generic utterances that can be memorized in the weights of the model when mapping from input utterance(s) to output, rather than employing recalled knowledge as context. Use of knowledge has so far proved difficult, in part because of the lack of a supervised learning benchmark task which exhibits knowledgeable open dialogue with clear grounding. To that end we collect and release a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia. We then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses. Our best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while our new benchmark allows for measuring further improvements in this important research direction.

Towards Language Agnostic Universal Representations    

tl;dr By formalizing universal grammar as an optimization problem we learn language agnostic universal representations which we can utilize to do zero-shot learning across languages.

When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in, even if the math lessons were only taught in one language. However, current representations in machine learning are language dependent. In this work, we present a method to decouple the language from the problem by learning language agnostic representations and therefore allowing training a model in one language and applying to a different one in a zero shot fashion. We learn these representations by taking inspiration from linguistics and formalizing Universal Grammar as an optimization process (Chomsky, 2014; Montague, 1970). We demonstrate the capabilities of these representations by showing that the models trained on a single language using language agnostic representations achieve very similar accuracies in other languages.

Training generative latent models by variational f-divergence minimization    

tl;dr Training generative models using an upper bound of the f divergence.

Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific form of $\f$-divergence between the model and data distribution. We derive an upper bound that holds for all $\f$-divergences, showing the intuitive result that the divergence between two joint distributions is at least as great as the divergence between their corresponding marginals. Additionally, the $\f$-divergence is not formally defined when two distributions have different supports. We thus propose a noisy version of $\f$-divergence which is well defined in such situations. We demonstrate how the bound and the new version of $\f$-divergence can be readily used to train complex probabilistic generative models of data and that the fitted model can depend significantly on the particular divergence used.

Learning-Based Frequency Estimation Algorithms    

tl;dr Data stream algorithms can be improved using deep learning, while retaining performance guarantees.

Estimating the frequencies of elements in a data stream is a fundamental task in data analysis and machine learning. The problem is typically addressed using streaming algorithms which can process very large data using limited storage. Today's streaming algorithms, however, cannot exploit patterns in their input to improve performance. We propose a new class of algorithms that automatically learn relevant patterns in the input data and use them to improve its frequency estimates. The proposed algorithms combine the benefits of machine learning with the formal guarantees available through algorithm theory. We prove that our learning-based algorithms have lower estimation errors than their non-learning counterparts. We also evaluate our algorithms on two real-world datasets and demonstrate empirically their performance gains.

Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference    

No tl;dr =[

Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations intentionally added to the original inputs can fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to extract the semantic features of the input and filter the non-semantic perturbation. FBGAN is pre-trained on the clean dataset in an unsupervised manner, adversarially learning a bidirectional mapping between a high-dimensional data space and a low-dimensional semantic space; also mutual information is applied to disentangle the semantically meaningful features. After the bidirectional mapping, the adversarial data can be reconstructed to denoised data, which could be fed into any pre-trained classifier. We empirically show the quality of reconstruction images and the effectiveness of defense.

Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity    

tl;dr Neural networks can be trained to modify their own connectivity, improving their online learning performance on challenging tasks.

The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning. Here we show for the first time that artificial neural networks with such neuromodulated plasticity can be trained with gradient descent. Extending previous work on differentiable Hebbian plasticity, we propose a differentiable formulation for the neuromodulation of plasticity. We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks. In one task, neuromodulated plastic LSTMs with millions of parameters outperform standard LSTMs on a benchmark language modeling task (controlling for the number of parameters). We conclude that differentiable neuromodulation of plasticity offers a powerful new framework for training neural networks.

Learn From Neighbour: A Curriculum That Train Low Weighted Samples By Imitating    

No tl;dr =[

Deep neural networks, which gain great success in a wide spectrum of applications, are often time, compute and storage hungry. Curriculum learning proposed to boost training of network by a syllabus from easy to hard. However, the relationship between data complexity and network training is unclear: why hard example harm the performance at beginning but helps at end. In this paper, we aim to investigate on this problem. Similar to internal covariate shift in network forward pass, the distribution changes in weight of top layers also affects training of preceding layers during the backward pass. We call this phenomenon inverse "internal covariate shift". Training hard examples aggravates the distribution shifting and damages the training. To address this problem, we introduce a curriculum loss that consists of two parts: a) an adaptive weight that mitigates large early punishment; b) an additional representation loss for low weighted samples. The intuition of the loss is very simple. We train top layers on "good" samples to reduce large shifting, and encourage "bad" samples to learn from "good" sample. In detail, the adaptive weight assigns small values to hard examples, reducing the influence of noisy gradients. On the other hand, the less-weighted hard sample receives the proposed representation loss. Low-weighted data gets nearly no training signal and can stuck in embedding space for a long time. The proposed representation loss aims to encourage their training. This is done by letting them learn a better representation from its superior neighbours but not participate in learning of top layers. In this way, the fluctuation of top layers is reduced and hard samples also received signals for training. We found in this paper that curriculum learning needs random sampling between tasks for better training. Our curriculum loss is easy to combine with existing stochastic algorithms like SGD. Experimental result shows an consistent improvement over several benchmark datasets.

Recurrent Experience Replay in Distributed Reinforcement Learning    

tl;dr Investigation on combining recurrent neural networks and experience replay leading to state-of-the-art agent on both Atari-57 and DMLab-30 using single set of hyper-parameters.

Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from experience replay. We investigate the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy. Using a single network architecture and fixed set of hyper-parameters, the resulting agent, Recurrent Replay Distributed DQN, triples the previous state of the art on Atari-57, and surpasses the state of the art on DMLab-30. R2D2 is the first agent to exceed human-level performance in 52 of the 57 Atari games.

Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling    

tl;dr We propose a simple modification to low-rank factorization that improves performances (in both image and language tasks) while still being compact.