A Correspondence Between Random Neural Networks and Statistical Field Theory

18 October 2017

Papers citing "A Correspondence Between Random Neural Networks and Statistical Field Theory"

12 / 12 papers shown

Title
Bayesian RG Flow in Neural Network Field Theories Jessica N. Howard Marc S. Klinger Anindita Maiti A. G. Stapleton 68 1 0 27 May 2024
Dynamical Isometry based Rigorous Fair Neural Architecture Search Jianxiang Luo Junyi Hu Tianji Pang Weihao Huang Chuan-Hsi Liu 21 0 0 05 Jul 2023
Renormalization in the neural network-quantum field theory correspondence Harold Erbin Vincent Lahoche D. O. Samary 42 7 0 22 Dec 2022
Nonperturbative renormalization for the neural network-QFT correspondence Harold Erbin Vincent Lahoche D. O. Samary 41 30 0 03 Aug 2021
Asymptotics of Wide Networks from Feynman Diagrams Ethan Dyer Guy Gur-Ari 29 113 0 25 Sep 2019
Novel Uncertainty Framework for Deep Learning Ensembles Tal Kachman Michal Moshkovitz Michal Rosen-Zvi UQCV OOD BDL 34 3 0 09 Apr 2019
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 244 349 0 14 Jun 2018
Deep learning generalizes because the parameter-function map is biased towards simple functions Guillermo Valle Pérez Chico Q. Camargo A. Louis MLT AI4CE 18 226 0 22 May 2018
Replica Symmetry Breaking in Bipartite Spin Glasses and Neural Networks Gavin Hartnett Edward Parker Edward Geist 15 23 0 17 Mar 2018
How to Start Training: The Effect of Initialization and Architecture Boris Hanin David Rolnick 19 253 0 05 Mar 2018
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Zhiwen Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 718 6,748 0 26 Sep 2016
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 186 1,186 0 30 Nov 2014