Approximation of Neural Networks for Verification

Statistical learning methods enable the adaptation of artificial neural networks (ANN) to complex problems. Meanwhile, formal properties can be verified on small ANNs under simplified assumptions. First we show a simple algorithm to convert neural networks into a system of equations with boundary conditions. In particular, we discuss how non-linear functions may be approximated. In experiments we study the impact of this approximation on the validity on the proof of formal guarantees.