30 research outputs found
RNNs Implicitly Implement Tensor Product Representations
Recurrent neural networks (RNNs) can learn continuous vector representations
of symbolic structures such as sequences and sentences; these representations
often exhibit linear regularities (analogies). Such regularities motivate our
hypothesis that RNNs that show such regularities implicitly compile symbolic
structures into tensor product representations (TPRs; Smolensky, 1990), which
additively combine tensor products of vectors representing roles (e.g.,
sequence positions) and vectors representing fillers (e.g., particular words).
To test this hypothesis, we introduce Tensor Product Decomposition Networks
(TPDNs), which use TPRs to approximate existing vector representations. We
demonstrate using synthetic data that TPDNs can successfully approximate linear
and tree-based RNN autoencoder representations, suggesting that these
representations exhibit interpretable compositional structure; we explore the
settings that lead RNNs to induce such structure-sensitive representations. By
contrast, further TPDN experiments show that the representations of four models
trained to encode naturally-occurring sentences can be largely approximated
with a bag of words, with only marginal improvements from more sophisticated
structures. We conclude that TPDNs provide a powerful method for interpreting
vector representations, and that standard RNNs can induce compositional
sequence representations that are remarkably well approximated by TPRs; at the
same time, existing training tasks for sentence representation learning may not
be sufficient for inducing robust structural representations.Comment: Accepted to ICLR 201
Learning Causal State Representations of Partially Observable Environments
Intelligent agents can cope with sensory-rich environments by learning task-agnostic state abstractions. In this paper, we propose mechanisms to approximate causal states, which optimally compress the joint history of actions and observations in partially-observable Markov decision processes. Our proposed algorithm extracts causal state representations from RNNs that are trained to predict subsequent observations given the history. We demonstrate that these learned task-agnostic state abstractions can be used to efficiently learn policies for reinforcement learning problems with rich observation spaces. We evaluate agents using multiple partially observable navigation tasks with both discrete (GridWorld) and continuous (VizDoom, ALE) observation processes that cannot be solved by traditional memory-limited methods. Our experiments demonstrate systematic improvement of the DQN and tabular models using approximate causal state representations with respect to recurrent-DQN baselines trained with raw inputs
Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces
We present a method to extract a weighted finite automaton (WFA) from a
recurrent neural network (RNN). Our algorithm is based on the WFA learning
algorithm by Balle and Mohri, which is in turn an extension of Angluin's
classic \lstar algorithm. Our technical novelty is in the use of
\emph{regression} methods for the so-called equivalence queries, thus
exploiting the internal state space of an RNN to prioritize counterexample
candidates. This way we achieve a quantitative/weighted extension of the recent
work by Weiss, Goldberg and Yahav that extracts DFAs. We experimentally
evaluate the accuracy, expressivity and efficiency of the extracted WFAs.Comment: AAAI 2020. We are preparing to distribute the implementatio