Overparameterization of deep ResNet: zero loss and mean-field analysis

30 May 2021

Papers citing "Overparameterization of deep ResNet: zero loss and mean-field analysis"

8 / 8 papers shown

Title
A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models Ziqing Xu Hancheng Min Salma Tarmoun Enrique Mallada Rene Vidal 9 0 0 16 May 2025
Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport Raphael Barboni Gabriel Peyré Franccois-Xavier Vialard 39 3 0 19 Mar 2024
Accelerating optimization over the space of probability measures Shi Chen Wenxuan Wu Yuhang Yao Stephen J. Wright 32 5 0 06 Oct 2023
High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance Krishnakumar Balasubramanian Promit Ghosal Ye He 40 5 0 03 Apr 2023
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 27 5 0 28 Oct 2022
On the Global Convergence of Gradient Descent for multi-layer ResNets in the mean-field regime Zhiyan Ding Shi Chen Qin Li S. Wright MLT AI4CE 43 11 0 06 Oct 2021
Representing smooth functions as compositions of near-identity functions with implications for deep network optimization Peter L. Bartlett S. Evans Philip M. Long 76 31 0 13 Apr 2018
Global optimality conditions for deep neural networks Chulhee Yun S. Sra Ali Jadbabaie 128 117 0 08 Jul 2017