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arXiv · 1801.05950

Toward Scalable Verification for Safety-Critical Deep Networks

Lindsey Kuper·Guy Katz·Justin Gottschlich·Kyle Julian·Clark Barrett·Mykel Kochenderfer
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Abstract

The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state of the art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification.

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Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark Barrett, Mykel Kochenderfer. 2018-02-02. Toward Scalable Verification for Safety-Critical Deep Networks. https://arxiv.org/abs/1801.05950

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