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
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How about: travelling salesman problem, uncertainty quantification, active tracking, graph classification, self-regularization ..?
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"deterministic finite automaton" has 2 results
tl;dr Finite Automata Can be Linearly decoded from Language-Recognizing RNNs (Show abstract)
We study the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language. Specifically, we train an RNN on positive and negative examples from a regular language, and ask if there is a simple decoding function that maps states of this RNN to states of the minimal deterministic finite automaton (MDFA) for the language. Our experiments show that such a decoding function exists, that it is in fact linear, but that it maps states of the RNN not to MDFA states, but to states of an abstraction obtained by clustering small sets of MDFA states into "superstates". A qualitative analysis reveals that the abstraction often has a simple interpretation. Overall, the results suggest a strong structural relationship between internal representations used by RNNs and finite automata, and explain the well-known ability of RNNs to recognize formal grammatical structure.
tl;dr We introduce stochastic state transition mechanism to RNNs, simplifies finite state automata (FDA) extraction, forces RNNs to operate more like automata with external memory, better extrapolation behavior and interpretability. (Show abstract)
Recurrent networks are a widely used class of neural architectures. They have, however, two shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We show that state-regularization (a) simplifies the extraction of finite state automata modeling an RNN's state transition dynamics, and (b) forces RNNs to operate more like automata with external memory and less like finite state machines.