A Rigorous Framework for the Mean Field Limit of Multilayer Neural Networks

30 January 2020

Papers citing "A Rigorous Framework for the Mean Field Limit of Multilayer Neural Networks"

22 / 22 papers shown

Title
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime Francesco Camilli D. Tieplova Eleonora Bergamin Jean Barbier 114 0 0 06 May 2025
Statistically guided deep learning Michael Kohler A. Krzyżak ODL BDL 79 0 0 11 Apr 2025
Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport Raphael Barboni Gabriel Peyré Franccois-Xavier Vialard 37 3 0 19 Mar 2024
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization Nuoya Xiong Lijun Ding Simon S. Du 35 11 0 03 Oct 2023
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss T. Getu Georges Kaddoum M. Bennis 40 1 0 13 Sep 2023
Depth Dependence of $μ$ P Learning Rates in ReLU MLPs Samy Jelassi Boris Hanin Ziwei Ji Sashank J. Reddi Srinadh Bhojanapalli Surinder Kumar 19 7 0 13 May 2023
Global Optimality of Elman-type RNN in the Mean-Field Regime Andrea Agazzi Jian-Xiong Lu Sayan Mukherjee MLT 34 1 0 12 Mar 2023
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron Weihang Xu S. Du 37 16 0 20 Feb 2023
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion Yangyi Liu Stefano Rini Sadaf Salehkalaibar Jun Chen FedML 21 4 0 23 Jan 2023
Uniform-in-time propagation of chaos for mean field Langevin dynamics Fan Chen Zhenjie Ren Song-bo Wang 45 30 0 06 Dec 2022
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 25 5 0 28 Oct 2022
Global Convergence of SGD On Two Layer Neural Nets Pulkit Gopalani Anirbit Mukherjee 26 5 0 20 Oct 2022
Analysis of the rate of convergence of an over-parametrized deep neural network estimate learned by gradient descent Michael Kohler A. Krzyżak 32 10 0 04 Oct 2022
On the universal consistency of an over-parametrized deep neural network estimate learned by gradient descent Selina Drews Michael Kohler 30 13 0 30 Aug 2022
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks Blake Bordelon Cengiz Pehlevan MLT 40 78 0 19 May 2022
On Feature Learning in Neural Networks with Global Convergence Guarantees Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 36 13 0 22 Apr 2022
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks A. Shevchenko Vyacheslav Kungurtsev Marco Mondelli MLT 38 13 0 03 Nov 2021
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 41 11 0 06 Oct 2021
Global Convergence of Three-layer Neural Networks in the Mean Field Regime H. Pham Phan-Minh Nguyen MLT AI4CE 41 19 0 11 May 2021
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network Mo Zhou Rong Ge Chi Jin 74 44 0 04 Feb 2021
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime Andrea Agazzi Jianfeng Lu 13 15 0 22 Oct 2020
Representation formulas and pointwise properties for Barron functions E. Weinan Stephan Wojtowytsch 23 79 0 10 Jun 2020