Extracting automata from recurrent neural networks using queries and counterexamples

Gail Weiss; Yoav Goldberg; Eran Yahav

Extracting automata from recurrent neural networks using queries and counterexamples

Gail Weiss, Yoav Goldberg, Eran Yahav

Technion-Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

43 Scopus citations

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.

Original language	English
Title of host publication	35th International Conference on Machine Learning, ICML 2018
Editors	Jennifer Dy, Andreas Krause
Publisher	International Machine Learning Society (IMLS)
Pages	8340-8349
Number of pages	10
ISBN (Electronic)	9781510867963
State	Published - 2018
Externally published	Yes
Event	35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden Duration: 10 Jul 2018 → 15 Jul 2018

Publication series

Name	35th International Conference on Machine Learning, ICML 2018
Volume	12

Conference

Conference	35th International Conference on Machine Learning, ICML 2018
Country/Territory	Sweden
City	Stockholm
Period	10/07/18 → 15/07/18

Bibliographical note

Publisher Copyright:
© 35th International Conference on Machine Learning, ICML 2018.All Rights Reserved.

Cite this

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title = "Extracting automata from recurrent neural networks using queries and counterexamples",

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.",

author = "Gail Weiss and Yoav Goldberg and Eran Yahav",

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year = "2018",

language = "אנגלית",

series = "35th International Conference on Machine Learning, ICML 2018",

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Weiss, G, Goldberg, Y & Yahav, E 2018, Extracting automata from recurrent neural networks using queries and counterexamples. in J Dy & A Krause (eds), 35th International Conference on Machine Learning, ICML 2018. 35th International Conference on Machine Learning, ICML 2018, vol. 12, International Machine Learning Society (IMLS), pp. 8340-8349, 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 10/07/18.

Extracting automata from recurrent neural networks using queries and counterexamples. / Weiss, Gail; Goldberg, Yoav; Yahav, Eran.
35th International Conference on Machine Learning, ICML 2018. ed. / Jennifer Dy; Andreas Krause. International Machine Learning Society (IMLS), 2018. p. 8340-8349 (35th International Conference on Machine Learning, ICML 2018; Vol. 12).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Extracting automata from recurrent neural networks using queries and counterexamples

Abstract

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Bibliographical note

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