Verifying Recurrent Neural Networks Using Invariant Inference

Yuval Jacoby*, Clark Barrett, Guy Katz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

Deep neural networks are revolutionizing the way complex systems are developed. However, these automatically-generated networks are opaque to humans, making it difficult to reason about them and guarantee their correctness. Here, we propose a novel approach for verifying properties of a widespread variant of neural networks, called recurrent neural networks. Recurrent neural networks play a key role in, e.g., speech recognition, and their verification is crucial for guaranteeing the reliability of many critical systems. Our approach is based on the inference of invariants, which allow us to reduce the complex problem of verifying recurrent networks into simpler, non-recurrent problems. Experiments with a proof-of-concept implementation of our approach demonstrate that it performs orders-of-magnitude better than the state of the art.

Original languageAmerican English
Title of host publicationAutomated Technology for Verification and Analysis - 18th International Symposium, ATVA 2020, Proceedings
EditorsDang Van Hung, Oleg Sokolsky
PublisherSpringer Science and Business Media Deutschland GmbH
Pages57-74
Number of pages18
ISBN (Print)9783030591519
DOIs
StatePublished - 2020
Event18th International Symposium on Automated Technology for Verification and Analysis, ATVA 2020 - Hanoi, Viet Nam
Duration: 19 Oct 202023 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12302 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Symposium on Automated Technology for Verification and Analysis, ATVA 2020
Country/TerritoryViet Nam
CityHanoi
Period19/10/2023/10/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

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