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Bisimulations for Neural Network Reduction

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Verification, Model Checking, and Abstract Interpretation (VMCAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13182))

Abstract

We present a notion of bisimulation that induces a reduced network which is semantically equivalent to the given neural network. We provide a minimization algorithm to construct the smallest bisimulation equivalent network. Reductions that construct bisimulation equivalent neural networks are limited in the scale of reduction. We present an approximate notion of bisimulation that provides semantic closeness, rather than, semantic equivalence, and quantify semantic deviation between the neural networks that are approximately bisimilar. The latter provides a trade-off between the amount of reduction and deviations in the semantics.

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Author information

Authors and Affiliations

  1. Kansas State University, Manhattan, KS, 66506, USA

    Pavithra Prabhakar

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  1. Pavithra Prabhakar
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Correspondence to Pavithra Prabhakar .

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Editors and Affiliations

  1. Helmholtz Center for Information Security, Saarbrücken, Germany

    Bernd Finkbeiner

  2. New York University, New York, NY, USA

    Thomas Wies

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Prabhakar, P. (2022). Bisimulations for Neural Network Reduction. In: Finkbeiner, B., Wies, T. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2022. Lecture Notes in Computer Science(), vol 13182. Springer, Cham. https://doi.org/10.1007/978-3-030-94583-1_14

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  • DOI: https://doi.org/10.1007/978-3-030-94583-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94582-4

  • Online ISBN: 978-3-030-94583-1

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