A Dynamical Central Limit Theorem for Shallow Neural Networks

A Dynamical Central Limit Theorem for Shallow Neural Networks

21 August 2020

Grant M. Rotskoff

Joan Bruna

Eric Vanden-Eijnden

Papers citing "A Dynamical Central Limit Theorem for Shallow Neural Networks"

7 / 7 papers shown

Title
Symmetries in Overparametrized Neural Networks: A Mean-Field View Javier Maass Martínez Joaquin Fontbona FedML MLT 50 2 0 30 May 2024
Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training Luís Carvalho Joao L. Costa José Mourao Gonccalo Oliveira AI4CE 18 1 0 06 Apr 2023
The Underlying Correlated Dynamics in Neural Training Rotem Turjeman Tom Berkov I. Cohen Guy Gilboa 19 3 0 18 Dec 2022
Convex Analysis of the Mean Field Langevin Dynamics Atsushi Nitanda Denny Wu Taiji Suzuki MLT 61 64 0 25 Jan 2022
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions Martin Hutzenthaler Arnulf Jentzen Katharina Pohl Adrian Riekert Luca Scarpa MLT 34 6 0 13 Dec 2021
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks Carles Domingo-Enrich A. Bietti Marylou Gabrié Joan Bruna Eric Vanden-Eijnden FedML 32 6 0 11 Jul 2021
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation Arnulf Jentzen Adrian Riekert 19 23 0 09 Jul 2021