160 citations · 834 across the 51 of their papers we have counts for
3 papers · 1 filter
A Dynamical Central Limit Theorem for Shallow Neural Networks
Zhengdao Chen, Grant M. Rotskoff, Joan Bruna +1
Recent theoretical works have characterized the dynamics of wide shallow neural networks trained via gradient descent in an asymptotic mean-field limit when the width tends towards…
Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization
Grant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden
Deep neural networks, when optimized with sufficient data, provide accurate representations of high-dimensional functions; in contrast, function approximation techniques that have…
A mean-field analysis of two-player zero-sum games
Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch +2
Finding Nash equilibria in two-player zero-sum continuous games is a central problem in machine learning, e.g. for training both GANs and robust models. The existence of pure Nash…