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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"Random selection" has 100 results

An Efficient Network for Predicting Time-Varying Distributions    

tl;dr We propose an efficient recurrent network model for forward prediction on time-varying distributions.

While deep neural networks have achieved groundbreaking prediction results in many tasks, there is a class of data where existing architectures are not optimal -- sequences of probability distributions. Performing forward prediction on sequences of distributions has many important applications. However, there are two main challenges in designing a network model for this task. First, neural networks are unable to encode distributions compactly as each node encodes just a real value. A recent work of Distribution Regression Network (DRN) solved this problem with a novel network that encodes an entire distribution in a single node, resulting in improved accuracies while using much fewer parameters than neural networks. However, despite its compact distribution representation, DRN does not address the second challenge, which is the need to model time dependencies in a sequence of distributions. In this paper, we propose our Recurrent Distribution Regression Network (RDRN) which adopts a recurrent architecture for DRN. The combination of compact distribution representation and shared weights architecture across time steps makes RDRN suitable for modeling the time dependencies in a distribution sequence. Compared to neural networks and DRN, RDRN achieves the best prediction performance while keeping the network compact.

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.

Trajectory VAE for multi-modal imitation    

tl;dr A trajectory-VAE method for imitating multi-modal expert demonstrations in sequential decision making problems.

We address the problem of imitating multi-modal expert demonstrations in sequential decision making problems. In many practical applications, for example video games, behavioural demonstrations are readily available that contain multi-modal structure not captured by typical existing imitation learning approaches. For example, differences in the observed players' behaviours may be representative of different underlying playstyles. In this paper, we use a generative model to capture different emergent playstyles in an unsupervised manner, enabling the imitation of a diverse range of distinct behaviours. We utilise a variational autoencoder to learn an embedding of the different types of expert demonstrations on the trajectory level, and jointly learn a latent representation with a policy. In experiments on a range of 2D continuous control problems representative of Minecraft environments, we empirically demonstrate that our model can capture a multi-modal structured latent space from the demonstrated behavioural trajectories.

Domain Adaptation for Structured Output via Disentangled Patch Representations    

tl;dr A domain adaptation method for structured output via learning patch-level discriminative feature representations

Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn strong supervised models like convolutional neural networks. However, these models trained on one data domain may not generalize well to other domains unequipped with annotations for model finetuning. To avoid the labor-intensive process of annotation, we develop a domain adaptation method to adapt the source data to the unlabeled target domain. To this end, we propose to learn discriminative feature representations of patches based on label histograms in the source domain, through the construction of a disentangled space. With such representations as guidance, we then use an adversarial learning scheme to push the feature representations in target patches to the closer distributions in source ones. In addition, we show that our framework can integrate a global alignment process with the proposed patch-level alignment and achieve state-of-the-art performance on semantic segmentation. Extensive ablation studies and experiments are conducted on numerous benchmark datasets with various settings, such as synthetic-to-real and cross-city scenarios.

Amortized Bayesian Meta-Learning    

tl;dr We propose a meta-learning method which efficiently amortizes hierarchical variational inference across training episodes.

Meta-learning, or learning-to-learn, has proven to be a successful strategy in attacking problems in supervised learning and reinforcement learning that involve small amounts of data. State-of-the-art solutions involve learning an initialization and/or learning algorithm using a set of training episodes so that the meta learner can generalize to an evaluation episode quickly. These methods perform well but often lack good quantification of uncertainty, which can be vital to real-world applications when data is lacking. We propose a meta-learning method which efficiently amortizes hierarchical variational inference across tasks, learning a prior distribution over neural network weights so that a few steps of Bayes by Backprop will produce a good task-specific approximate posterior. We show that our method produces good uncertainty estimates on contextual bandit and few-shot learning benchmarks.

Environment Probing Interaction Policies    

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A key challenge in reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment. A common approach to improve inter-environment transfer is to learn policies that are invariant to the distribution of testing environments. However, we argue that instead of being invariant, the policy should identify the specific nuances of an environment and exploit them to achieve better performance. In this work, we propose the “Environment-Probing” Interaction (EPI) policy, a policy that probes a new environment to extract an implicit understanding of that environment’s behavior. Once this environment-specific information is obtained, it is used as an additional input to a task-specific policy that can now perform environment-conditioned actions to solve a task. To learn these EPI-policies, we present a reward function based on transition predictability. Specifically, a higher reward is given if the trajectory generated by the EPI-policy can be used to better predict transitions. We experimentally show that EPI-conditioned task-specific policies significantly outperform commonly used policy generalization methods on novel testing environments.

h-detach: Modifying the LSTM Gradient Towards Better Optimization    

tl;dr A simple algorithm to improve optimization and handling of long term dependencies in LSTM.

Recurrent neural networks are known for their notorious exploding and vanishing gradient problem (EVGP). This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because EVGP prevents important gradient components from being back-propagated adequately over a large number of steps. We introduce a simple stochastic algorithm h-detach that is specific to LSTM optimization and targeted towards addressing this problem. Specifically, we show that when the LSTM weights are large, the gradient components through the linear path (cell state) in the LSTM computational graph get suppressed. Based on the hypothesis that these components carry information about long term dependencies (which we show empirically), their suppression can prevent the LSTM from capturing them. Our algorithm prevents the gradients flowing through this path from getting suppressed, thus allowing the LSTM to capture such dependencies better. We show significant convergence and generalization improvements using our algorithm on various benchmark datasets.

Open Loop Hyperparameter Optimization and Determinantal Point Processes    

tl;dr We address fully parallel hyperparameter optimization with Determinantal Point Processes.

Driven by the need for parallelizable hyperparameter optimization methods, this paper studies open loop search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampling distributions. In particular, we propose the use of k-determinantal point processes in hyperparameter optimization via random search. Compared to conventional uniform random search where hyperparameter settings are sampled independently, a k-DPP promotes diversity. We describe an approach that transforms hyperparameter search spaces for efficient use with a k-DPP. In addition, we introduce a novel Metropolis-Hastings algorithm which can sample from k-DPPs defined over any space from which uniform samples can be drawn, including spaces with a mixture of discrete and continuous dimensions or tree structure. Our experiments show significant benefits in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.

Probabilistic Recursive Reasoning for Mutli-Agent Reinforcement Learning    

tl;dr We proposed a novel probabilisitic recursive reasoning (PR2) framework for multi-agent deep reinforcement learning tasks.

Humans are capable of attributing latent mental contents such as beliefs, or intentions to others. The social skill is critical in everyday life to reason about the potential consequences of their behaviors so as to plan ahead. It is known that humans use this reasoning ability recursively, i.e. considering what others believe about their own beliefs. In this paper, we introduce a probabilistic recursive reasoning (PR2) framework for multi-agent reinforcement learning (RL). Our hypothesis is that it is beneficial for each agent to consider how the opponents would react to its future behaviors. Under the PR2 framework, we adopt variational Bayes methods to approximate the opponents' conditional policy, to which each agent finds the best response and then improve their own policy. We develop decentralized-training-decentralized-execution algorithms, PR2-Q and PR2-Actor-Critic, that are proved to converge in the self-play scenario. Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge. Our experiments show that it is critical to reason about how the opponents believe about what the agent believes. We expect our work to offer a new idea of embedding opponent modeling into the multi-agent RL context.

RANDOM MASK: Towards Robust Convolutional Neural Networks    

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Robustness of neural networks has recently been highlighted by the adversarial examples, i.e., inputs added with well-designed perturbations which are imperceptible to humans but can cause the network to give incorrect outputs. In this paper, we design a new CNN architecture that by itself has good robustness. We introduce a simple but powerful technique, Random Mask, to modify existing CNN structures. We show that CNN with Random Mask achieves state-of-the-art performance against black-box adversarial attacks without applying any adversarial training. We next investigate the adversarial examples which “fool” a CNN with Random Mask. Surprisingly, we find that these adversarial examples often “fool” humans as well. This raises fundamental questions on how to define adversarial examples and robustness properly.

Generative predecessor models for sample-efficient imitation learning    

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We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observed in expert demonstrations, using generative models to reason probabilistically about alternative histories of demonstrated states. We show that this approach allows an agent to learn robust policies using only a small number of expert demonstrations and self-supervised interactions with the environment. We derive this approach from first principles and compare it empirically to a state-of-the-art imitation learning method, showing that it outperforms or matches its performance on two simulated robot manipulation tasks and demonstrate significantly higher sample efficiency by applying the algorithm on a real robot.

Bamboo: Ball-Shape Data Augmentation Against Adversarial Attacks from All Directions    

tl;dr The first data augmentation method specially designed for improving the general robustness of DNN without any hypothesis on the attacking algorithms.

Deep neural networks (DNNs) are widely adopted in real-world cognitive applications because of their high accuracy. The robustness of DNN models, however, has been recently challenged by adversarial attacks where small disturbance on input samples may result in misclassification. State-of-the-art defending algorithms, such as adversarial training or robust optimization, improve DNNs' resilience to adversarial attacks by paying high computational costs. Moreover, these approaches are usually designed to defend one or a few known attacking techniques only. The effectiveness to defend other types of attacking methods, especially those that have not yet been discovered or explored, cannot be guaranteed. This work aims for a general approach of enhancing the robustness of DNN models under adversarial attacks. In particular, we propose Bamboo -- the first data augmentation method designed for improving the general robustness of DNN without any hypothesis on the attacking algorithms. Bamboo augments the training data set with a small amount of data uniformly sampled on a fixed radius ball around each training data and hence, effectively increase the distance between natural data points and decision boundary. Our experiments show that Bamboo substantially improve the general robustness against arbitrary types of attacks and noises, achieving better results comparing to previous adversarial training methods, robust optimization methods and other data augmentation methods with the same amount of data points.

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.

Rethinking the Value of Network Pruning    

tl;dr In network pruning, fine-tuning a pruned model only gives comparable or worse performance than training it from scratch.

Network pruning is widely used for reducing the heavy computational cost of deep networks. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning, and each of the three stages is considered as indispensable. In this work, we make several surprising observations which contradict common beliefs. For all the six state-of-the-art pruning algorithms we examined, fine-tuning a pruned model only gives comparable or even worse performance than training that model with randomly initialized weights. For pruning algorithms which assume a predefined architecture of the target pruned network, one can completely get rid of the pipeline and directly train the target network from scratch. Our observations are consistent for a wide variety of pruning algorithms with multiple network architectures, datasets and tasks. Our results have several implications: 1) training an over-parameterized model is not necessary to obtain an efficient final model, 2) learned ``important'' weights of the large model are not necessarily helpful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited ``important'' weights, is what leads to the efficiency benefit in the final model, which suggests that some pruning algorithms could be seen as performing network architecture search.

Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders    

tl;dr In this work we propose deep inside-outside recursive auto-encoders(DIORA) a fully unsupervised method of discovering syntax while simultaneously learning representations for discovered constituents.

Syntax is a powerful abstraction for language understanding. Many downstream tasks require segmenting input text into meaningful constituent chunks (e.g., noun phrases or entities); more generally, models for learning semantic representations of text benefit from integrating syntax in the form of parse trees (e.g., tree-LSTMs). Supervised parsers have traditionally been used to obtain these trees, but lately interest has increased in unsupervised methods that induce syntactic representations directly from unlabeled text. To this end, we propose the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Unlike many prior approaches, DIORA does not rely on supervision from auxiliary downstream tasks and is thus not constrained to particular domains. Furthermore, competing approaches do not learn explicit phrase representations along with tree structures, which limits their applicability to phrase-based tasks. Extensive experiments on unsupervised parsing, segmentation, and phrase clustering demonstrate the efficacy of our method. DIORA achieves the state of the art in unsupervised parsing (46.9 F1) on the benchmark WSJ dataset.

Hierarchical Reinforcement Learning with Limited Policies and Hindsight    

tl;dr We propose a new hierarchical RL framework that can improve performance in tasks involving long time horizons and sparse rewards.

We introduce a new hierarchical reinforcement learning framework that can accelerate learning in tasks involving long time horizons and sparse rewards. Our approach improves sample efficiency by enabling agents to learn a hierarchy of short policies that operate at different time scales. The policy hierarchies can support an arbitrary number of levels, and all policies within the hierarchy are trained in parallel and end-to-end. Our framework is the first hierarchical reinforcement learning approach that can learn hierarchies with more than two levels of policies in continuous tasks. We demonstrate experimentally in both grid world and simulated robotics domains that our approach can significantly boost sample efficiency. A video illustrating our results is available at

Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-valued Inverse Reinforcement Learning    

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In recent years, reinforcement learning methods have been applied to model gameplay with great success, achieving super-human performance in various environments, such as Atari, Go and Poker. However, those studies mostly focus on winning the game and have largely ignored the rich and complex human motivations, which are essential for understanding the agents' diverse behavior. In this paper, we present a multi-motivation behavior modeling which investigates the multifaceted human motivations and models the underlying value structure of the agents. Our approach extends inverse RL to the vectored-valued setting which imposes a much weaker assumption than previous studies. The vectorized rewards incorporate Pareto optimality, which is a powerful tool to explain a wide range of behavior by its optimality. For practical assessment, our algorithm is tested on the World of Warcraft Avatar History dataset spanning three years of the gameplay. Our experiments demonstrate the improvement over the scalarization-based methods on real-world problem settings.

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.

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.

Reasoning About Physical Interactions with Object-Centric Models    

tl;dr We present a framework for learning object-centric representations suitable for planning in tasks that require an understanding of physics.

Object-based factorizations provide a useful level of abstraction for interacting with the world. Building explicit object representations, however, often requires supervisory signals that are difficult to obtain in practice. We present a paradigm for learning object-centric representations for physical scene understanding without direct supervision of object properties. Our model, object-oriented prediction and planning (O2P2), jointly learns a perception function to map from image observations to object representations, a pairwise physics interaction function to predict the time evolution of a collection of objects, and a rendering function to map objects back to pixels. For evaluation, we consider not only the accuracy of the physical predictions of the model, but also its utility for downstream tasks that require an actionable representation of intuitive physics. After training our model on an image prediction task, we can use its learned representations to build block towers more complicated than those observed during training.

Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching    

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We consider the problem of training speech recognition systems without using any labeled data, under the assumption that the learner can only access to the input utterances and a phoneme language model estimated from a non-overlapping corpus. We propose a fully unsupervised learning algorithm that alternates between solving two sub-problems: (i) learn a phoneme classifier for a given set of phoneme segmentation boundaries, and (ii) refining the phoneme boundaries based on a given classifier. To solve the first sub-problem, we introduce a novel unsupervised cost function named Segmental Empirical Output Distribution Matching, which generalizes the work in (Liu et al., 2017) to segmental structures. For the second sub-problem, we develop an approximate MAP approach to refining the boundaries obtained from Wang et al. (2017). Experimental results on TIMIT dataset demonstrate the success of this first fully unsupervised phoneme recognition system, which achieves a phone error rate (PER) of 41.6%. Although it is still far away from the state-of-the-art supervised systems, we show that with oracle boundaries and matching language model, the PER could be improved to 32.5%. This performance approaches the supervised system of the same model architecture, demonstrating the great potential of the proposed method.

Cost-Sensitive Robustness against Adversarial Examples    

tl;dr A general method for training certified cost-sensitive robust classifier against adversarial perturbations

Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. However, these methods assume that all the adversarial transformations provide equal value for adversaries, which is seldom the case in real-world applications. We advocate for cost-sensitive robustness as the criteria for measuring the classifier's performance for specific tasks. We encode the potential harm of different adversarial transformations in a cost matrix, and propose a general objective function to adapt the robust training method of Wong & Kolter (2018) to optimize for cost-sensitive robustness. Our experiments on simple MNIST and CIFAR10 models and a variety of cost matrices show that the proposed approach can produce models with substantially reduced cost-sensitive robust error, while maintaining classification accuracy.

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.

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space    

tl;dr A new state-of-the-art approach for knowledge graph embedding.

We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the relations. In this paper, we present a new approach for knowledge graph embedding called RotatE, which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model is not only scalable, but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction.

Probabilistic Program Induction for Intuitive Physics Game Play    

tl;dr The paper describes a method imitating human cognition about the physical world to play games in environments of physical interactions.

Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the physics of objects. We propose a framework for bots to deploy similar tools for interacting with intuitive physics environments. The framework employs a physics simulation in a probabilistic way to infer about moves performed by an agent in a setting governed by Newtonian laws of motion. However, methods of probabilistic programs can be slow in such setting due to their need to generate many samples. We complement the model with a model-free approach to aid the sampling procedures in becoming more efficient through learning from experience during game playing. We present an approach where a myriad of model-free approaches (a convolutional neural network in our model) and model-based approaches (probabilistic physics simulation) is able to achieve what neither could alone. This way the model outperforms an all model-free or all model-based approach. We discuss a case study showing empirical results of the performance of the model on the game of Flappy Bird.

Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings    

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Word embeddings are known to boost performance of many NLP tasks such as text classification, meanwhile they can be enhanced by labels at the document level to capture nuanced meaning such as sentiment and topic. Can one combine these two research directions to benefit from both? In this paper, we propose to jointly train a text classifier with a label-enhanced and domain-aware word embedding model, using an unlabeled corpus and only a few labeled data from non-target domains. The embeddings are trained on the unlabed corpus and enhanced by pseudo labels coming from the classifier, and at the same time are used by the classifier as input and training signals. We formalize this symbiotic cycle in a variational Bayes framework, and show that our method improves both the embeddings and the text classifier, outperforming state-of-the-art domain adaptation and semi-supervised learning techniques. We conduct detailed ablative tests to reveal gains from important components of our approach. The source code and experiment data will be publicly released.

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.

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.

Overlapping Community Detection with Graph Neural Networks    

tl;dr Detecting overlapping communities in graphs using graph neural networks

Community detection in graphs is of central importance in graph mining, machine learning and network science. Detecting overlapping communities is especially challenging, and remains an open problem. Motivated by the success of graph-based deep learning in other graph-related tasks, we study the applicability of this framework for overlapping community detection. We propose a probabilistic model for overlapping community detection based on the graph neural network architecture. Despite its simplicity, our model outperforms the existing approaches in the community recovery task by a large margin. Moreover, due to the inductive formulation, the proposed model is able to perform out-of-sample community detection for nodes that were not present at training time

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.

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.

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.

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.


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.

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.

LayoutGAN: Generating Graphic Layouts with Wireframe Discriminator    

No tl;dr =[

Layouts are important for graphic design and scene generation. We propose a novel generative adversarial network, named as LayoutGAN, that synthesizes graphic layouts by modeling semantic and geometric relations of 2D elements. The generator of LayoutGAN takes as input a set of randomly placed 2D graphic elements and uses self-attention modules to refine their semantic and geometric parameters jointly to produce a meaningful layout. Accurate alignment is critical for good layouts. We thus propose a novel differentiable wireframe rendering layer that maps the generated layout to a wireframe image, upon which a CNN-based discriminator is used to optimize the layouts in visual domain. We validate the effectiveness of LayoutGAN in various experiments including MNIST digit generation, document layout generation, clipart abstract scene generation and tangram graphic design.

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.

Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning    

tl;dr We studied exploration with imperfect planning and used object representation to learn simple models and introduced a new sample efficient RL algorithm that achieves state of the art results on Pitfall!

Humans learn to play video games significantly faster than the state-of-the-art reinforcement learning (RL) algorithms. People seem to build simple models that are easy to learn to support planning and strategic exploration. Inspired by this, we investigate two issues in leveraging model-based RL for sample efficiency. First we investigate how to perform strategic exploration when exact planning is not feasible and empirically show that optimistic Monte Carlo Tree Search outperforms posterior sampling methods. Second we show how to learn simple deterministic models to support fast learning using object representation. We illustrate the benefit of these ideas by introducing a novel algorithm, Strategic Object Oriented Reinforcement Learning (SOORL), that outperforms state-of-the-art algorithms in the game of Pitfall! in less than 50 episodes.

Predictive Local Smoothness for Stochastic Gradient Methods    

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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.

Object-Oriented Model Learning through Multi-Level Abstraction    

No tl;dr =[

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.

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.

Parametrizing Fully Convolutional Nets with a Single High-Order Tensor    

No tl;dr =[

Recent findings indicate that over-parametrization, while crucial to the success of deep learning, also introduces large amounts of redundancy. Tensor methods have the potential to parametrize over-complete representations in a compact manner by leveraging this redundancy. In this paper, we propose fully parametrizing Convolutional Neural Networks (CNNs) with a single, low-rank tensor. Previous works on network tensorization haved focused on parametrizing individual layers (convolutional or fully connected) only, and perform the tensorization layer-by-layer disjointly. In contrast, we propose to jointly capture the full structure of a CNN by parametrizing it with a single, high-order tensor, the modes of which represent each of the architectural design parameters of the CNN (e.g. number of convolutional blocks, depth, number of stacks, input features, etc). This parametrization allows to regularize the whole network and drastically reduce the number of parameters by imposing a low-rank structure on that tensor. Further, our network is end-to-end trainable from scratch, which has been shown to be challenging in prior work. We study the case of networks with rich structure, namely Fully Convolutional CNNs, which we propose to parametrize them with a single 8-dimensional tensor. We show that our approach can achieve superior performance with small compression rates, and attain high compression rates with negligible drop in accuracy for the challenging task of human pose estimation.

Incremental training of multi-generative adversarial networks    

tl;dr We propose a new method to incrementally train a mixture generative model to approximate the information projection of the real data distribution.

Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set. However, a determinate input distribution as well as a specific architecture of neural networks may impose limitations on capturing the diversity in the high dimensional target space. To resolve this difficulty, we propose a training framework that greedily produce a series of generative adversarial networks that incrementally capture the diversity of the target space. We show theoretically and empirically that our training algorithm converges to the theoretically optimal distribution, the projection of the real distribution onto the convex hull of the network's distribution space.

PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention    

tl;dr An autoregressive deep learning model for generating diverse point clouds.

A point cloud is an agile 3D representation, efficiently modeling an object's surface geometry. However, these surface-centric properties also pose challenges on designing tools to recognize and synthesize point clouds. This work presents a novel autoregressive model, PointGrow, which generates realistic point cloud samples from scratch or conditioned from given semantic contexts. Our model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points. Since point cloud object shapes are typically encoded by long-range interpoint dependencies, we augment our model with dedicated self-attention modules to capture these relations. Extensive evaluation demonstrates that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to fidelity, diversity and semantic preservation. Further, conditional PointGrow learns a smooth manifold of given images where 3D shape interpolation and arithmetic calculation can be performed inside.

Robustness and Equivariance of Neural Networks    

tl;dr Robustness to rotations comes at the cost of robustness of pixel-wise adversarial perturbations.

Neural networks models are known to be vulnerable to geometric transformations as well as small pixel-wise perturbations of input. Convolutional Neural Networks (CNNs) are translation-equivariant but can be easily fooled using rotations and small pixel-wise perturbations. Moreover, CNNs require sufficient translations in their training data to achieve translation-invariance. Recent work by Cohen & Welling (2016), Worrall et al. (2016), Kondor & Trivedi (2018), Cohen & Welling (2017), Marcos et al. (2017), and Esteves et al. (2018) has gone beyond translations, and constructed rotation-equivariant or more general group-equivariant neural network models. In this paper, we do an extensive empirical study of various rotation-equivariant neural network models to understand how effectively they learn rotations. This includes Group-equivariant Convolutional Networks (GCNNs) by Cohen & Welling (2016), Harmonic Networks (H-Nets) by Worrall et al. (2016), Polar Transformer Networks (PTN) by Esteves et al. (2018) and Rotation equivariant vector field networks by Marcos et al. (2017). We empirically compare the ability of these networks to learn rotations efficiently in terms of their number of parameters, sample complexity, rotation augmentation used in training. We compare them against each other as well as Standard CNNs. We observe that as these rotation-equivariant neural networks learn rotations, they instead become more vulnerable to small pixel-wise adversarial attacks, e.g., Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), in comparison with Standard CNNs. In other words, robustness to geometric transformations in these models comes at the cost of robustness to small pixel-wise perturbations.

CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks    

tl;dr We introduce CNNSAT, a fast and accurate statistical decision procedure for SAT based on convolutional neural networks.

Boolean satisfiability (SAT) is one of the most well-known NP-complete problems and has been extensively studied. State-of-the-art solvers exist and have found a wide range of applications. However, they still do not scale well to formulas with hundreds of variables. To tackle this fundamental scalability challenge, we introduce CNNSAT, a fast and accurate statistical decision procedure for SAT based on convolutional neural networks. CNNSAT's effectiveness is due to a precise and compact representation of Boolean formulas. On both real and synthetic formulas, CNNSAT is highly accurate and orders of magnitude faster than the state-of-the-art solver Z3. We also describe how to extend CNNSAT to predict satisfying assignments when it predicts a formula to be satisfiable.

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.

Neural Regression Tree    

tl;dr A novel neural regression tree for optimal discretization in regression-via-classification problems.

Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. Current approaches for RvC use ad-hoc discretization strategies and are suboptimal. We propose a neural regression tree model for RvC. In this model, we employ a joint optimization framework where we learn optimal discretization thresholds while simultaneously optimizing the features for each node in the tree. We empirically show the validity of our model by testing it on two challenging regression tasks where we establish the state of the art.

Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs    

tl;dr Language generation using seq2seq models which produce word embeddings instead of a softmax based distribution over the vocabulary at each step enabling much faster training while maintaining generation quality

The Softmax function is used in the final layer of nearly all existing sequence-to-sequence models for language generation. However, it is usually the slowest layer to compute which limits the vocabulary size to a subset of most frequent types; and it has a large memory footprint. We propose a general technique for replacing the softmax layer with a continuous embedding layer. Our primary innovations are a novel probabilistic loss, and a training and inference procedure in which we generate a probability distribution over pre-trained word embeddings, instead of a multinomial distribution over the vocabulary obtained via softmax. We evaluate this new class of sequence-to-sequence models with continuous outputs on the task of neural machine translation. We show that our models obtain upto 2.5x speed-up in training time while performing on par with the state-of-the-art models in terms of translation quality. These models are capable of handling very large vocabularies without compromising on translation quality. They also produce more meaningful errors than in the softmax-based models, as these errors typically lie in a subspace of the vector space of the reference translations.

Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification    

tl;dr Meta-learning task-agnostic representations with attention.

Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding. Inspired by the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification. The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms. We provide evidence to show that the attention mechanism in ATAML has a synergistic effect on learning performance. Our experimental results reveal that, for few-shot text classification tasks, gradient-based meta-learning approaches ourperform popular transfer learning methods. In comparisons with models trained from random initialization, pretrained models and meta trained MAML, our proposed ATAML method generalizes better on single-label and multi-label classification tasks in miniRCV1 and miniReuters-21578 datasets.

Improving machine classification using human uncertainty measurements    

tl;dr improving classifiers using human uncertainty measurements

As deep CNN classifier performance using ground-truth labels has begun to asymptote at near-perfect levels, a key aim for the field is to extend training paradigms to capture further useful structure in natural image data and improve model robustness and generalization. In this paper, we present a novel natural image benchmark for making this extension, which we call CIFAR10H. This new dataset comprises a human-derived, full distribution over labels for each image of the CIFAR10 test set, offering the ability to assess the generalization of state-of-the-art CIFAR10 models, as well as investigate the effects of including this information in model training. We show that classification models trained on CIFAR10 do not generalize as well to our dataset as it does to traditional extensions, and that models fine-tuned using our label information are able to generalize better to related datasets, complement popular data augmentation schemes, and provide robustness to adversarial attacks. We explain these improvements in terms of better empirical approximations to the expected loss function over natural images and their categories in the visual world.

Activity Regularization for Continual Learning    

tl;dr This paper develops a novel regularization for continual learning

While deep neural networks have achieved remarkable successes, they suffer the well-known catastrophic forgetting issue when switching from existing tasks to tackle a new one. In this paper, we study continual learning with deep neural networks that learn from tasks arriving sequentially. We first propose an approximated multi-task learning framework that unifies a family of popular regularization based continual learning methods. We then analyze the weakness of existing approaches, and propose a novel regularization method named “Activity Regularization” (AR), which alleviates forgetting meanwhile keeping model’s plasticity to acquire new knowledge. Extensive experiments show that our method outperform state-of-the-art methods and effectively overcomes catastrophic forgetting.

Evolving intrinsic motivations for altruistic behavior    

tl;dr We introduce a biologically-inspired modular evolutionary algorithm in which deep RL agents learn to cooperate in a difficult multi-agent social game, which could help to explain the evolution of altruism.

Multi-agent cooperation is an important feature of the natural world. Many tasks involve individual incentives that are misaligned with the common good, yet a wide range of organisms from bacteria to insects and humans are able to overcome their differences and collaborate. Therefore, the emergence of cooperative behavior amongst self-interested individuals is an important question for the fields of multi-agent reinforcement learning (MARL) and evolutionary theory. Here, we study a particular class of multi-agent problems called intertemporal social dilemmas (ISDs), where the conflict between the individual and the group is particularly sharp. By combining MARL with appropriately structured natural selection, we demonstrate that individual inductive biases for cooperation can be learned in a model-free way. To achieve this, we introduce an innovative modular architecture for deep reinforcement learning agents which supports multi-level selection. We present results in two challenging environments, and interpret these in the context of cultural and ecological evolution.

Unsupervised Word Discovery with Segmental Neural Language Models    

tl;dr A LSTM language model that discovers words from unsegmented sequences of characters.

We propose a segmental neural language model that combines the representational power of neural networks and the structure learning mechanism of Bayesian nonparametrics, and show that it learns to discover semantically meaningful units (e.g., morphemes and words) from unsegmented character sequences. The model generates text as a sequence of segments, where each segment is generated either character-by-character from a sequence model or as a single draw from a lexical memory that stores multi-character units. Its parameters are fit to maximize the marginal likelihood of the training data, summing over all segmentations of the input, and its hyperparameters are likewise set to optimize held-out marginal likelihood. To prevent the model from overusing the lexical memory, which leads to poor generalization and bad segmentation, we introduce a differentiable regularizer that penalizes based on the expected length of each segment. To our knowledge, this is the first demonstration of neural networks that have predictive distributions better than LSTM language models and also infer a segmentation into word-like units that are competitive with the best existing word discovery models.

Logit Regularization Methods for Adversarial Robustness    

tl;dr Logit regularization methods help explain and improve state of the art adversarial defenses

While great progress has been made at making neural networks effective across a wide range of tasks, many are surprisingly vulnerable to small, carefully chosen perturbations of their input, known as adversarial examples. In this paper, we advocate for and experimentally investigate the use of logit regularization techniques as an adversarial defense, which can be used in conjunction with other methods for creating adversarial robustness at little to no cost. We demonstrate that much of the effectiveness of one recent adversarial defense mechanism can be attributed to logit regularization and show how to improve its defense against both white-box and black-box attacks, in the process creating a stronger black-box attacks against PGD-based models.

Modulating transfer between tasks in gradient-based meta-learning    

tl;dr We use the connection between gradient-based meta-learning and hierarchical Bayes to learn a mixture of meta-learners that is appropriate for a heterogeneous and evolving task distribution.

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task. This approach encounters difficulty when transfer is not mutually beneficial, for instance, when tasks are sufficiently dissimilar or change over time. Here, we use the connection between gradient-based meta-learning and hierarchical Bayes to propose a mixture of hierarchical Bayesian models over the parameters of an arbitrary function approximator such as a neural network. Generalizing the model-agnostic meta-learning (MAML) algorithm, we present a stochastic expectation maximization procedure to jointly estimate parameter initializations for gradient descent as well as a latent assignment of tasks to initializations. This approach better captures the diversity of training tasks as opposed to consolidating inductive biases into a single set of hyperparameters. Our experiments demonstrate better generalization on the standard miniImageNet benchmark for 1-shot classification. We further derive a novel and scalable non-parametric variant of our method that captures the evolution of a task distribution over time as demonstrated on a set of synthetic regression tasks as well as an evolving dataset adapted from miniImageNet.

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.

Using Ontologies To Improve Performance In Massively Multi-label Prediction    

tl;dr We propose a new method for using ontology information to improve performance on massively multi-label prediction/classification problems.

Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples for the rare labels. We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels. We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision.

Graph Classification with Geometric Scattering    

tl;dr We present a new feed forward graph ConvNet based on generalizing the wavelet scattering transform of Mallat, and demonstrate its utility in graph classification tasks.

One of the most notable contributions of deep learning is the application of convolutional neural networks (ConvNets) to structured signal classification, and in particular image classification. Beyond their impressive performances in supervised learning, the structure of such networks inspired the development of deep filter banks referred to as scattering transforms. These transforms apply a cascade of wavelet transforms and complex modulus operators to extract features that are invariant to group operations and stable to deformations. Furthermore, ConvNets inspired recent advances in geometric deep learning, which aim to generalize these networks to graph data by applying notions from graph signal processing to learn deep graph filter cascades. We further advance these lines of research by proposing a geometric scattering transform using graph wavelets defined in terms of random walks on the graph. We demonstrate the utility of features extracted with this designed deep filter bank in graph classification, and show its competitive performance relative to other methods, including graph kernel methods and geometric deep learning ones, on both social and biochemistry data.

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset    

tl;dr We train a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure, enabled by the new MAESTRO dataset.

Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments. Herein, we show that by using notes as an intermediate representation, we can train a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure on timescales spanning six orders of magnitude (~0.1 ms to ~100 s). This large advance in the state of the art is enabled by our release of the new MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) dataset, composed of over 172 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms. The networks and the dataset together present a promising approach toward creating new expressive and interpretable neural models of music.

Stochastic Learning of Additive Second-Order Penalties with Applications to Fairness    

tl;dr We propose a method to stochastically optimize second-order penalties and show how this may apply to training fairness-aware classifiers.

Many notions of fairness may be expressed as linear constraints, and the resulting constrained objective is often optimized by transforming the problem into its Lagrangian dual with additive linear penalties. In non-convex settings, the resulting problem may be difficult to solve as the Lagrangian is not guaranteed to have a deterministic saddle-point equilibrium. In this paper, we propose to modify the linear penalties to second-order ones, and we argue that this results in a more practical training procedure in non-convex, large-data settings. For one, the use of second-order penalties allows training the penalized objective with a fixed value of the penalty coefficient, thus avoiding the instability and potential lack of convergence associated with two-player min-max games. Secondly, we derive a method for efficiently computing the gradients associated with the second-order penalties in stochastic mini-batch settings. Our resulting algorithm performs well empirically, learning an appropriately fair classifier on a number of standard benchmarks.

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.

L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data    

tl;dr We develop two linear-complexity algorithms for model-agnostic model interpretation based on the Shapley value, in the settings where the contribution of features to the target is well-approximated by a graph-structured factorization.

Instancewise feature scoring is a method for model interpretation, which yields, for each test instance, a vector of importance scores associated with the feature vector. Methods based on the Shapley score have been proposed as a fair way of computing feature attributions of this kind, but incur an exponential complexity in the number of features for black-box models. This combinatorial explosion arises from the definition of the Shapley value and prevents these methods from being scalable to large data sets and complex models. We focus on settings in which the data have a graph structure, and the contribution of features to the target variable is well-approximated by a graph-structured factorization. In such settings, we develop two algorithms with linear complexity for instancewise feature importance scoring on black-box models. We establish the relationship of our methods to the Shapley value and a closely related concept known as the Myerson value from cooperative game theory. We demonstrate on both language and image data that our algorithms compare favorably with other methods for model interpretation.

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication    

No tl;dr =[

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.

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.

3D-RelNet: Joint Object and Relational Network for 3D Prediction    

tl;dr We reason about relative spatial relationships between the objects in a scene to produce better 3D predictions

We propose an approach to predict the 3D shape and pose for the objects present in a scene. Existing learning based methods that pursue this goal make independent predictions per object, and do not leverage the relationships amongst them. We argue that reasoning about these relationships is crucial, and present an approach to incorporate these in a 3D prediction framework. In addition to independent per-object predictions, we predict pairwise relations in the form of relative 3D pose, and demonstrate that these can be easily incorporated to improve object level estimates. We report performance across different datasets (SUNCG, NYUv2), and show that our approach significantly improves over independent prediction approaches while also outperforming alternate implicit reasoning methods.

Q-map: a Convolutional Approach for Goal-Oriented Reinforcement Learning    

tl;dr Q-map is a reinforcement learning agent that uses a convolutional autoencoder-like architecture to efficiently learn to navigate its environment.

Goal-oriented learning has become a core concept in the reinforcement learning (RL) framework, extending the reward signal as a sole way to define tasks. Generalized value functions (GVFs) utilize an array of independent value functions, each trained for a specific goal, while universal value function approximators (UVFAs) enable generalization between goals by providing them in input. As parameterizing value functions with goals increases the learning complexity, efficiently reusing past experience to update estimates towards several goals at once becomes desirable, but requires independent updates per goal for both GVFs and UVFAs. Considering that a significant number of RL environments can support spatial coordinates as goals - such as on-screen location of the character in ATARI or SNES games, we propose a novel goal-oriented agent called Q-map that utilizes an autoencoder-like neural network to predict the minimum number of steps towards each coordinate in a single forward pass. This architecture is similar to Horde with parameter sharing and allows the agent to discover correlations between visual patterns and navigation. For example learning how to use a ladder in a game could be transferred to other ladders later. We show how this network can be efficiently trained with a 3D variant of Q-learning to update the estimates towards all goals at once. While the Q-map agent could be used for a wide range of applications, we propose a novel exploration mechanism in place of epsilon-greedy that relies on goal selection at a predicted target distance followed by several steps taken towards it, thus allowing the agent to take much longer and coherent exploratory steps in the environment. We demonstrate the accuracy and generalization qualities of the Q-map agent on a grid-world environment and then demonstrate how the proposed exploration mechanism allows the agent to explore much further than random walks on the notoriously difficult Montezuma's Revenge game and finally show how the combination of Q-map with a task-learner DQN agent improves the performance on the Super Mario All-Stars game.

MLPrune: Multi-Layer Pruning for Automated Neural Network Compression    

tl;dr MLPrune: an automated pruning method that doesn't require any tuning for per-layer compression ratio, achieves state-of-the-art pruning results on AlexNet and VGG16.

Model compression can significantly reduce the computation and memory footprint of large neural networks. To achieve a good trade-off between model size and accuracy, popular compression techniques usually rely on hand-crafted heuristics and require manually setting the compression ratio of each layer. This process is typically costly and suboptimal. In this paper, we propose a Multi-Layer Pruning method (MLPrune), which is theoretically sound, and can automatically decide appropriate compression ratios for all layers. Towards this goal, we use an efficient approximation of the Hessian as our pruning criterion, based on a Kronecker-factored Approximate Curvature method. We demonstrate the effectiveness of our method on several datasets and architectures, outperforming previous state-of-the-art by a large margin. Our experiments show that we can compress AlexNet and VGG16 by 25x without loss in accuracy on ImageNet. Furthermore, our method has much fewer hyper-parameters and requires no expert knowledge.

The Cakewalk Method    

tl;dr A new policy gradient algorithm designed to approach black-box combinatorial optimization problems. The algorithm relies only on function evaluations, and returns locally optimal solutions with high probability.

Combinatorial optimization is a common theme in computer science. While in general such problems are NP-Hard, from a practical point of view, locally optimal solutions can be useful. In some combinatorial problems however, it can be hard to define meaningful solution neighborhoods that connect large portions of the search space, thus hindering methods that search this space directly. We suggest to circumvent such cases by utilizing a policy gradient algorithm that transforms the problem to the continuous domain, and to optimize a new surrogate objective that renders the former as generic stochastic optimizer. This is achieved by producing a surrogate objective whose distribution is fixed and predetermined, thus removing the need to fine-tune various hyper-parameters in a case by case manner. Since we are interested in methods which can successfully recover locally optimal solutions, we use the problem of finding locally maximal cliques as a challenging experimental benchmark, and we report results on a large dataset of graphs that is designed to test clique finding algorithms. Notably, we show in this benchmark that fixing the distribution of the surrogate is key to consistently recovering locally optimal solutions, and that our surrogate objective leads to an algorithm that outperforms other methods we have tested in a number of measures.

Skip-gram word embeddings in hyperbolic space    

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Embeddings of tree-like graphs in hyperbolic space were recently shown to surpass their Euclidean counterparts in performance by a large margin. Inspired by these results, we present an algorithm for learning word embeddings in hyperbolic space from free text. An objective function based on the hyperbolic distance is derived and included in the skip-gram negative-sampling architecture from word2vec. The hyperbolic word embeddings are then evaluated on word similarity and analogy benchmarks. The results demonstrate the potential of hyperbolic word embeddings, particularly in low dimensions, though without clear superiority over their Euclidean counterparts. We further discuss subtleties in the formulation of the analogy task in curved spaces.

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.

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.


tl;dr first posing and solving the sample efficiency optimization problem in the non-parameterized policy space, and then solving a supervised regression problem to find a parameterized policy that is near the optimal non-parameterized policy.

We propose a new sample-efficient methodology, called Supervised Policy Update (SPU), for deep reinforcement learning. Starting with data generated by the current policy, SPU formulates and solves a constrained optimization problem in the non-parameterized proximal policy space. Using supervised regression, it then converts the optimal non-parameterized policy to a parameterized policy, from which it draws new samples. The methodology is general in that it applies to both discrete and continuous action spaces, and can handle a wide variety of proximity constraints for the non-parameterized optimization problem. We show how the Natural Policy Gradient and Trust Region Policy Optimization (NPG/TRPO) problems, and the Proximal Policy Optimization (PPO) problem can be addressed by this methodology. The SPU implementation is much simpler than TRPO. In terms of sample efficiency, our extensive experiments show SPU outperforms TRPO in Mujoco simulated robotic tasks and outperforms PPO in Atari video game tasks.

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.

End-to-End Multi-Lingual Multi-Speaker Speech Recognition    

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The expressive power of end-to-end automatic speech recognition (ASR) systems enables direct estimation of the character or word label sequence from a sequence of acoustic features. Direct optimization of the whole system is advantageous because it not only eliminates the internal linkage necessary for hybrid systems, but also extends the scope of potential application use cases by training the model for multiple objectives. Several multi-lingual ASR systems were recently proposed based on a monolithic neural network architecture without language-dependent modules, showing that modeling of multiple languages is well within the capabilities of an end-to-end framework. There has also been growing interest in multi-speaker speech recognition, which enables generation of multiple label sequences from single-channel mixed speech. In particular, a multi-speaker end-to-end ASR system that can directly model one-to-many mappings without additional auxiliary clues was recently proposed. In this paper, we propose an all-in-one end-to-end multi-lingual multi-speaker ASR system that integrates the capabilities of these two systems. The proposed model is evaluated using mixtures of two speakers generated by using 10 languages, including mixed-language utterances.

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.

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.

How Powerful are Graph Neural Networks?    

tl;dr We develop theoretical foundations for expressive power of GNNs and design a provably most powerful GNN.

Graph Neural Networks (GNNs) for representation learning of graphs broadly follow a neighborhood aggregation framework, where the representation vector of a node is computed by recursively aggregating and transforming feature vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs in capturing different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance.

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.

Improving Sample-based Evaluation for Generative Adversarial Networks    

tl;dr This paper improves existing sample-based evaluation for GANs and contains some insightful experiments.

In this paper, we propose an improved quantitative evaluation framework for Generative Adversarial Networks (GANs) on generating domain-specific images, where we improve conventional evaluation methods on two levels: the feature representation and the evaluation metric. Unlike most existing evaluation frameworks which transfer the representation of ImageNet inception model to map images onto the feature space, our framework uses a specialized encoder to acquire fine-grained domain-specific representation. Moreover, for datasets with multiple classes, we propose Class-Aware Frechet Distance (CAFD), which employs a Gaussian mixture model on the feature space to better fit the multi-manifold feature distribution. Experiments and analysis on both the feature level and the image level were conducted to demonstrate improvements of our proposed framework over the recently proposed state-of-the-art FID method. To our best knowledge, we are the first to provide counter examples where FID gives inconsistent results with human judgments. It is shown in the experiments that our framework is able to overcome the shortness of FID and improves robustness. Code will be made available.

SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities    

tl;dr A method to model the generative distribution of sequences coming from graph connected entities.

We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are trained in order to exploit the knowledge of the graph structure and in such a way that the obtained mixtures tend to be sparse. Experiments in different application domains demonstrate the effectiveness and versatility of the method.

Local Binary Pattern Networks for Character Recognition    

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Memory and computation efficient deep learning architectures are crucial to the continued proliferation of machine learning capabilities to new platforms and systems. Binarization of operations in convolutional neural networks has shown promising results in reducing the model size and computing efficiency. In this paper, we tackle the character recognition problem using a strategy different from the existing literature by proposing local binary pattern networks or LBPNet that can learn and perform bit-wise operations in an end-to-end fashion. LBPNet uses local binary comparisons and random projection in place of conventional convolution (or approximation of convolution) operations, providing important means to improve memory and speed efficiency that is particularly suited for small footprint devices and hardware accelerators. These operations can be implemented efficiently on different platforms including direct hardware implementation. LBPNet demonstrates its particular advantage on the character classification task where the content is composed of strokes. We applied LBPNet to benchmark datasets like MNIST, SVHN, DHCD, ICDAR, and Chars74K and observed encouraging results.

Initialized Equilibrium Propagation for Backprop-Free Training    

tl;dr We train a feedforward network without backprop by using an energy-based model to provide local targets

Deep neural networks are almost universally trained with reverse-mode automatic differentiation (a.k.a. backpropagation). Biological networks, on the other hand, appear to lack any mechanism for sending gradients back to their input neurons, and thus cannot be learning in this way. In response to this, Scellier & Bengio (2017) proposed Equilibrium Propagation - a method for gradient-based train- ing of neural networks which uses only local learning rules and, crucially, does not rely on neurons having a mechanism for back-propagating an error gradient. Equilibrium propagation, however, has a major practical limitation: inference involves doing an iterative optimization of neural activations to find a fixed-point, and the number of steps required to closely approximate this fixed point scales poorly with the depth of the network. In response to this problem, we propose Initialized Equilibrium Propagation, which trains a feedforward network to initialize the iterative inference procedure for Equilibrium propagation. This feed-forward network learns to approximate the state of the fixed-point using a local learning rule. After training, we can simply use this initializing network for inference, resulting in a learned feedforward network. Our experiments show that this network appears to work as well or better than the original version of Equilibrium propagation. This shows how we might go about training deep networks without using backpropagation.

Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation    

tl;dr We propose a method of transferring knowledge between related RL tasks using visual mappings, and demonstrate its effectiveness on visual variants of the Atari Breakout game and different levels of Road Fighter, a Nintendo car driving game.

Deep Reinforcement Learning has managed to achieve state-of-the-art results in learning control policies directly from raw pixels. However, despite its remarkable success, it fails to generalize, a fundamental component required in a stable Artificial Intelligence system. Using the Atari game Breakout, we demonstrate the difficulty of a trained agent in adjusting to simple modifications in the raw image, ones that a human could adapt to trivially. In transfer learning, the goal is to use the knowledge gained from the source task to make the training of the target task faster and better. We show that using various forms of fine-tuning, a common method for transfer learning, is not effective for adapting to such small visual changes. In fact, it is often easier to re-train the agent from scratch than to fine-tune a trained agent. We suggest that in some cases transfer learning can be improved by adding a dedicated component whose goal is to learn to visually map between the known domain and the new one. Concretely, we use Unaligned Generative Adversarial Networks (GANs) to create a mapping function to translate images in the target task to corresponding images in the source task. These mapping functions allow us to transform between various variations of the Breakout game, as well as between different levels of a Nintendo game, Road Fighter. We show that learning this mapping is substantially more efficient than re-training. A visualization of a trained agent playing Breakout and Road Fighter, with and without the GAN transfer, can be seen in \url{} and \url{}.

ADef: an Iterative Algorithm to Construct Adversarial Deformations    

tl;dr We propose a new, efficient algorithm to construct adversarial examples by means of deformations, rather than additive perturbations.

While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood. In the past, image classifiers have been shown to be vulnerable to so-called adversarial attacks, which are created by additively perturbing the correctly classified image. In this paper, we propose the ADef algorithm to construct a different kind of adversarial attack created by iteratively applying small deformations to the image, found through a gradient descent step. We demonstrate our results on MNIST with convolutional neural networks and on ImageNet with Inception-v3 and ResNet-101.

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.

Adversarial Attacks on Graph Neural Networks via Meta Learning    

tl;dr We use meta-gradients to attack the training procedure of deep neural networks for graphs.

Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks, essentially treating the graph as a hyperparameter to optimize. Our experiments show that small graph perturbations consistently lead to a strong decrease in performance for graph convolutional networks, and even transfer to unsupervised embeddings. Remarkably, the perturbations created by our algorithm misguide the graph neural networks such that they perform worse than a simple baseline that ignores all relational information. Our attacks do not assume any knowledge about or access to the target classifiers.

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

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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.

Novel positional encodings to enable tree-structured transformers    

tl;dr We develop novel positional encodings for tree-structured data, enabling transformers to be applied to tree structured problems.

With interest in program synthesis and similarly flavored problems rapidly increasing, neural models optimized for tree-domain problems are of great value. In the sequence domain, transformers can learn relationships across arbitrary pairs of positions with less bias than recurrent models. Under the intuition that a similar property would be beneficial in the tree domain, we propose a method to extend transformers to tree-structured inputs and/or outputs. Our approach abstracts transformer’s default sinusoidal positional encodings, allowing us to substitute in a novel custom positional encoding scheme that represents node positions within a tree. We evaluated our model in tree-to-tree program translation settings, achieving superior performance to other models from the literature on several tasks.

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.


tl;dr Large batch size training using adversarial training and second order information

Stochastic Gradient Descent (SGD) methods using randomly selected batches are widely-used to train neural network (NN) models. Performing design exploration to find the best NN for a particular task often requires extensive training with different models on a large dataset, which is very computationally expensive. The most straightforward method to accelerate this computation is to distribute the batch of SGD over multiple processors. However, large batch training often times leads to degradation in accuracy, poor generalization, and even poor robustness to adversarial attacks. Existing solutions for large batch training either do not work or require massive hyper-parameter tuning. To address this issue, we propose a novel large batch training method which combines recent results in adversarial training (to regularize against ``sharp minima'') and second order optimization (to use curvature information to change batch size adaptively during training). We extensively evaluate our method on Cifar-10/100, SVHN, TinyImageNet, and ImageNet datasets, using multiple NNs, including residual networks as well as compressed networks such as SqueezeNext. Our new approach exceeds the performance of the existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $3\times$, respectively). We emphasize that this is achieved without any additional hyper-parameter tuning to tailor our method to any of these experiments.

Understand the dynamics of GANs via Primal-Dual Optimization    

tl;dr We show that, with a proper stepsize choice, the widely used first-order iterative algorithm in training GANs would in fact converge to a stationary solution with a sublinear rate.

Generative adversarial network (GAN) is one of the best known unsupervised learning techniques these days due to its superior ability to learn data distributions. In spite of its great success in applications, GAN is known to be notoriously hard to train. The tremendous amount of time it takes to run the training algorithm and its sensitivity to hyper-parameter tuning have been haunting researchers in this area. To resolve these issues, we need to first understand how GANs work. Herein, we take a step toward this direction by examining the dynamics of GANs. We relate a large class of GANs including the Wasserstein GANs to max-min optimization problems with the coupling term being linear over the discriminator. By developing new primal-dual optimization tools, we show that, with a proper stepsize choice, the widely used first-order iterative algorithm in training GANs would in fact converge to a stationary solution with a sublinear rate. The same framework also applies to multi-task learning and distributional robust learning problems. We verify our analysis on numerical examples with both synthetic and real data sets. We hope our analysis shed light on future studies on the theoretical properties of relevant machine learning problems.

What Is in a Translation Unit? Comparing Character and Subword Representations Beyond Translation    

tl;dr We study the impact of using different kinds of subword units on the quality of the resulting representations when used to model syntax, semantics, and morphology.

Recent work has shown that contextualized word representations derived from neural machine translation (NMT) are a viable alternative to such from simple word predictions tasks. This is because the internal understanding that needs to be built in order to be able to translate from one language to another is much more comprehensive. Unfortunately, computational and memory limitations as of present prevent NMT models from using large word vocabularies, and thus alternatives such as subword units (BPE and morphological segmentations) and characters have been used. Here we study the impact of using different kinds of units on the quality of the resulting representations when used to model syntax, semantics, and morphology. We found that while representations derived from subwords are slightly better for modeling syntax, character-based representations are superior for modeling morphology and are also more robust to noisy input.

Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring    

tl;dr We propose the Prob2Vec method for problem embedding used in a personalized e-learning tool in addition to a data level classification method, called negative pre-training, for cases where the training data set is imbalanced.

We propose a new application of embedding techniques to problem retrieval in adaptive tutoring. The objective is to retrieve problems similar in mathematical concepts. There are two challenges: First, like sentences, problems helpful to tutoring are never exactly the same in terms of the underlying concepts. Instead, good problems mix concepts in innovative ways, while still displaying continuity in their relationships. Second, it is difficult for humans to determine a similarity score consistent across a large enough training set. We propose a hierarchical problem embedding algorithm, called Prob2Vec, that consists of an abstraction and an embedding step. Prob2Vec achieves 96.88\% accuracy on a problem similarity test, in contrast to 75\% from directly applying state-of-the-art sentence embedding methods. It is surprising that Prob2Vec is able to distinguish very fine-grained differences among problems, an ability humans need time and effort to acquire. In addition, the sub-problem of concept labeling with imbalanced training data set is interesting in its own right. It is a multi-label problem suffering from dimensionality explosion, which we propose ways to ameliorate. We propose the novel negative pre-training algorithm that dramatically reduces false negative and positive ratios for classification, using an imbalanced training data set.

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.

Representation Flow for Action Recognition    

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In this paper, we propose a convolutional layer inspired by optical flow algorithms to learn motion representations. Our representation flow layer is a fully-differentiable layer designed to optimally capture the '`flow' of any representation channel within a convolutional neural network. Its parameters for iterative flow optimization are learned in an end-to-end fashion together with the other model parameters, maximizing the action recognition performance. Furthermore, we newly introduce the concept of learning '`flow of flow' representations by stacking multiple representation flow layers. We conducted extensive experimental evaluations, confirming its advantages over previous recognition models using traditional optical flows in both computational speed and performance.

Model Compression with Generative Adversarial Networks    

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The ever-increasing accuracy of machine learning models often comes at the expense of higher computational costs and memory requirements at test time, making them impractical to deploy on memory-constrained or CPU-constrained devices. Model compression (also known as distillation) is a technique to compress a complex model into a simpler one while maintaining most of the original accuracy. This can be done by using the same dataset for both the model training and compression tasks or by exploiting additional data. However, in many real-world applications, additional data are not available, and the repeated use of the original training data leads to suboptimal compression. In this work, we propose to use generative adversarial networks (GANs) to approximately sample from the distribution of the original data, thus generating ''unlimited'' synthetic data that can be used to perform the compression task. Our GAN-assisted model compression approach shows significant improvement in compressing complex models such as deep neural networks and large random forests on both image and tabular datasets. Furthermore, based on the model compression results, we propose a comprehensive metric—the Compression Score—to evaluate the quality of generative models, which captures both the discriminability and the diversity of the synthetic data. We show that the Compression Score performs well in cases when the popular Inception Score fails.

Deep Convolutional Networks as shallow Gaussian Processes    

tl;dr We show that CNNs and ResNets with appropriate priors on the parameters are Gaussian processes in the limit of infinitely many convolutional filters.

We show that the output of a (residual) CNN with an appropriate prior over the weights and biases is a GP in the limit of infinitely many convolutional filters, extending similar results for dense networks. For a CNN, the equivalent kernel can be computed exactly and, unlike "deep kernels", has very few parameters: only the hyperparameters of the original CNN. Further, we show that this kernel has two properties that allow it to be computed efficiently; the cost of evaluating the kernel for a pair of images is similar to a single forward pass through the original CNN with only one filter per layer. The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on MNIST, a new record for GP with a comparable number of parameters.


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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.

Learning to Remember More with Less Memorization    

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Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize that this scheme of writing is suboptimal in memory utilization and introduces redundant computation. To validate our hypothesis, we derive a theoretical bound on the amount of information stored in a RAM-like system and formulate an optimization problem that maximizes the bound. The proposed solution dubbed Uniform Writing is proved to be optimal under the assumption of equal timestep contributions. To relax this assumption, we introduce modifications to the original solution, resulting in a solution termed Cached Uniform Writing. This method aims to balance between maximizing memorization and forgetting via overwriting mechanisms. Through an extensive set of experiments, we empirically demonstrate the advantages of our solutions over other recurrent architectures, claiming the state-of-the-arts in various sequential modeling tasks.