2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Verification of Neural Networks against Convolutional Perturbations via Parameterised Kernels
Benedikt Brückner, Alessio Lomuscio
We develop a method for the efficient verification of neural networks against convolutional perturbations such as blurring or sharpening. To define input perturbations we use well-…
cs.LG2024
Tightening the Evaluation of PAC Bounds Using Formal Verification Results
Thomas Walker, Alessio Lomuscio
Probably Approximately Correct (PAC) bounds are widely used to derive probabilistic guarantees for the generalisation of machine learning models. They highlight the components of t…
cs.LG2024★ 2 cited
Tight Verification of Probabilistic Robustness in Bayesian Neural Networks
Ben Batten, Mehran Hosseini, Alessio Lomuscio
We introduce two algorithms for computing tight guarantees on the probabilistic robustness of Bayesian Neural Networks (BNNs). Computing robustness guarantees for BNNs is a signifi…