Scalable Polyhedral Verification of Recurrent Neural Networks

1 code implementation • 27 May 2020 • Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, Martin Vechev

We present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and nonlinear recurrent update functions by combining sampling, optimization, and Fermat's theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron.

Certifying Neural Network Audio Classifiers

no code implementations • 25 Sep 2019 • Wonryong Ryou, Mislav Balunovic, Gagandeep Singh, Martin Vechev

We present the first end-to-end verifier of audio classifiers.

Audio Classification