This talk was presented as part of JuliaCon2021
Deep neural networks are widely used for nonlinear function approximation, with applications ranging from computer vision to control. Although these networks involve the composition of simple arithmetic operations, it can be very challenging to verify whether a particular network satisfies certain input-output properties. NeuralVerification.jl implements several methods that have emerged recently for soundly verifying such properties. We discuss fundamental differences between existing algorithms and compare them on a set of benchmark problems.
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