3 accepted papers
Understanding generalization in overparameterized neural networks hinges on the interplay between the data geometry, neural architecture, and training dynamics. In this paper, we theoretically explore how data geometry controls this implicit bias. This paper presents theoretical results for overpara…
We study the implicit bias of flatness / low (loss) curvature and its effects on generalization in two-layer overparameterized ReLU networks with multivariate inputs---a problem well motivated by the minima stability and edge-of-stability phenomena in gradient-descent training. Existing work either…
We study a family of inverse problems in which a continuous-domain object is reconstructed from a finite number of noisy linear measurements. We study regularization methods for solving these problems in which the regularizers promote sparsity in the frequency domain. We show that sparse superpositi…