Publications [#378740] of Rong Ge

Papers Published

1. Ren, Y; Zhou, M; Ge, R, DEPTH SEPARATION WITH MULTILAYER MEAN-FIELD NETWORKS, 11th International Conference on Learning Representations Iclr 2023 (January, 2023)
   (last updated on 2026/01/15)

   Abstract:
   Depth separation-why a deeper network is more powerful than a shallower one-has been a major problem in deep learning theory. Previous results often focus on representation power. For example, Safran et al. (2019) constructed a function that is easy to approximate using a 3-layer network but not approximable by any 2-layer network. In this paper, we show that this separation is in fact algorithmic: one can learn the function constructed by Safran et al. (2019) using an overparameterized network with polynomially many neurons efficiently. Our result relies on a new way of extending the mean-field limit to multilayer networks, and a decomposition of loss that factors out the error introduced by the discretization of infinite-width mean-field networks.