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Regular Inference on Artificial Neural Networks

Abstract : This paper explores the general problem of explaining the behavior of artificial neural networks (ANN). The goal is to construct a representation which enhances human understanding of an ANN as a sequence classifier, with the purpose of providing insight on the rationale behind the classification of a sequence as positive or negative, but also to enable performing further analyses, such as automata-theoretic formal verification. In particular, a probabilistic algorithm for constructing a deterministic finite automaton which is approximately correct with respect to an artificial neural network is proposed.
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https://hal.inria.fr/hal-02998138
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Submitted on : Tuesday, November 10, 2020 - 1:47:48 PM
Last modification on : Wednesday, November 11, 2020 - 3:29:51 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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Franz Mayr, Sergio Yovine. Regular Inference on Artificial Neural Networks. 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2018, Hamburg, Germany. pp.350-369, ⟨10.1007/978-3-319-99740-7_25⟩. ⟨hal-02998138⟩

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