Mathematical Approaches to Deep Learning: Model Uncertainty, Robustness and Regularization

Tuesday, November 10, 2020 - 12:30pm - 1:15pm
Adam Oberman (McGill University)
Deep learning is a hot area, but many of the results are empirical and short-lived. Experts in the area have asked for contributions from mathematics to bring some rigour to the area. In this talk I will describe problems where a mathematical approach has been effective. The problems are: (i) deep model uncertainty, (ii) certified robust models, and (iii) regularized neural ODEs. The talk will be geared to a broad audience: I’ll explain the problems in context, and present the ideas at a high level.