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arXiv:1711.09576cs.LG

Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

Gail Weiss, Yoav Goldberg, Eran Yahav

Abstract

We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this using Angluin's L* algorithm as a learner and the trained RNN as an oracle. Our technique efficiently extracts accurate automata from trained RNNs, even when the state vectors are large and require fine differentiation.

arXiv comments
Accepted in ICML 2018, (Feb 2020: added link to code, at https://github.com/tech-srl/lstar_extraction )
Journal reference
ICML 2018
DOI
Subjects
cs.LGMachine Learning cs.FLFormal Languages and Automata Theory

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Linked by 2 identifiable commenters in 2 comments across 2 threads, from May 2018 to Oct 2020. Found in 1 HN submission from Nov 2017 to Nov 2017. Open submission discussions.

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