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arXiv · 1910.12396

Simplifying Neural Networks using Formal Verification

Sumathi Gokulanathan·Alexander Feldsher·Adi Malca·Clark Barrett·Guy Katz
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Abstract

Deep neural network (DNN) verification is an emerging field, with diverse verification engines quickly becoming available. Demonstrating the effectiveness of these engines on real-world DNNs is an important step towards their wider adoption. We present a tool that can leverage existing verification engines in performing a novel application: neural network simplification, through the reduction of the size of a DNN without harming its accuracy. We report on the work-flow of the simplification process, and demonstrate its potential significance and applicability on a family of real-world DNNs for aircraft collision avoidance, whose sizes we were able to reduce by as much as 10%.

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Sumathi Gokulanathan, Alexander Feldsher, Adi Malca, Clark Barrett, Guy Katz. 2019-10-25. Simplifying Neural Networks using Formal Verification. https://arxiv.org/abs/1910.12396

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