May 2019

NeuralVerificaiton.jl: Algorithms for verifying deep neural networks

Authors:

C. Liu, T. Arnon, C. Lazarus, and M. Kochenderfer

Workshop Paper

Abstract:

Deep neural networks (DNNs) are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although DNNs involve the composition of simple arithmetic operations, it can be very challenging to verify whether a particular network satisfies certain input-output properties.

This work introduces NeuralVerification.jl, a software package that implements
methods that have emerged recently for soundly verifying such properties. These
methods borrow insights from reachability analysis, optimization, and search. We
present the formal problem definition and briefly discuss the fundamental differences between the implemented algorithms. In addition, we provide a pedagogical example of how to use the library.

Notes:

Best Applied Paper Award

@workshop{Liu-2019-119899,
author = {C. Liu And T. Arnon And C. Lazarus And M. Kochenderfer},
title = {NeuralVerificaiton.jl: Algorithms for verifying deep neural networks},
booktitle = {Proceedings of ICLR '19 Debugging Machine Learning Models Workshop},
year = {2019},
month = {May},
}