Extending Neural Network Verification to a Larger Family of Piece-wise
  Linear Activation Functions
László Antal Hana Masara Erika Ábrahám
In this paper, we extend an available neural network verification technique
to support a wider class of piece-wise linear activation functions.
Furthermore, we extend the algorithms, which provide in their original form
exact respectively over-approximative results for bounded input sets
represented as start sets, to allow also unbounded input set. We implemented
our algorithms and demonstrated their effectiveness in some case studies.