Robustness and interpretability of Bayesian neural networks
Guido Sanguinetti (SISSA, Trieste, Italy)
Friday 26th March 15:00-16:00 ZOOM: https://www.smartsurvey.co.uk/s/MNW32H/
Abstract: Deep neural networks have surprised the world in the last decade with their successes in a number of difficult machine learning tasks. However, while their successes are now part of everyday life, DNNs also exhibit some profound weaknesses: chief amongst them, in my opinion, their black box nature and brittleness under adversarial attacks. In this talk, I will discuss a geometric perspective which sheds light on the origins of their vulnerability under adversarial attack, and has also considerable implications for their interpretability. I will also show how a Bayesian treatment of DNNs provably avoids adversarial weaknesses, and improves interpretability (in a saliency context).
Refs: Carbone et al, NeurIPS 2020 https://arxiv.org/abs/2002.04359
Carbone et al, under review, https://arxiv.org/abs/2102.11010