Related papers: Efficient compilation of expressive problem space specifications to neural network solvers

Efficient compilation of expressive problem space specifications to neural network solvers

URL: http://arxiv.org/abs/2402.01353v1
Date: Wed, 24 Jan 2024 09:13:09 GMT
Title: Efficient compilation of expressive problem space specifications to neural network solvers
Authors: Matthew L. Daggitt, Wen Kokke, Robert Atkey
Abstract summary: We describe an algorithm for compiling the former to the latter. We explore and overcome complications that arise from targeting neural network solvers as opposed to standard SMT solvers.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Recent work has described the presence of the embedding gap in neural network verification. On one side of the gap is a high-level specification about the network's behaviour, written by a domain expert in terms of the interpretable problem space. On the other side are a logically-equivalent set of satisfiability queries, expressed in the uninterpretable embedding space in a form suitable for neural network solvers. In this paper we describe an algorithm for compiling the former to the latter. We explore and overcome complications that arise from targeting neural network solvers as opposed to standard SMT solvers.

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