3 accepted papers
We improve the efficacy of bound-propagation-based neural network verification by reducing the computational effort required by state-of-the-art propagation methods without incurring any loss in precision. We propose a method that infers the stability of ReLU nodes at every step of the back-substitu…
We introduce the problem of training neural networks such that they are robust against a class of smooth intensity perturbations modelled by bias fields. We first develop an approach towards this goal based on a state-of-the-art robust training method utilising Interval Bound Propagation (IBP). We a…
We propose a novel, complete algorithm for the verification and analysis of feed-forward, ReLU-based neural networks. The algorithm, based on symbolic interval propagation, introduces a new method for determining split-nodes which evaluates the indirect effect that splitting has on the relaxations o…