Logical Approximation and Formal Verification of Neural Networks

Explainability and formal verification of neural networks may be crucial when using these models to perform critical tasks. Pursuing explainability properties, we present a method for approximating neural networks by piecewise linear functions, which is a step to achieve a logical representation of the network. We also explain how such logical representations may be applied in the formal verification of some properties of neural networks. Furthermore, we present the results of an empirical experiment where the methods introduced are used in a case study.