Deep-Learning Theory and Generalization

Deep neural networks can contain large numbers of parameters and nevertheless generalize well beyond their training data. We study the mechanisms underlying this behavior and the relationship between optimization, learned representations, and generalization.

Our research in deep learning theory focuses on generalization, training dynamics, and the geometry of neural networks. These questions help us distinguish phenomena that genuinely contribute to generalization from properties that merely occur alongside it.