Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Gail Weiss · Yoav Goldberg · Eran Yahav
2018 Poster
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 \lstar 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.
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