Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Kernel Renormalization

7 December 2020

Papers citing "Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Kernel Renormalization"

26 / 26 papers shown

Title
Deep Neural Nets as Hamiltonians Mike Winer Boris Hanin 360 0 0 31 Mar 2025
Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer Blake Bordelon Cengiz Pehlevan AI4CE 140 1 0 04 Feb 2025
Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks B. Annesi Enrico M. Malatesta Francesco Zamponi 71 3 0 09 Oct 2024
Bayesian RG Flow in Neural Network Field Theories Jessica N. Howard Marc S. Klinger Anindita Maiti A. G. Stapleton 90 2 0 27 May 2024
Connecting NTK and NNGP: A Unified Theoretical Framework for Wide Neural Network Learning Dynamics Yehonatan Avidan Qianyi Li H. Sompolinsky 92 8 0 08 Sep 2023
Finite size corrections for neural network Gaussian processes J. Antognini BDL 44 30 0 27 Aug 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization T. Poggio Andrzej Banburski Q. Liao ODL 67 164 0 25 Aug 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 80 634 0 14 Aug 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 93 352 0 27 Mar 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 193 1,638 0 28 Dec 2018
A mathematical theory of semantic development in deep neural networks Andrew M. Saxe James L. McClelland Surya Ganguli 73 270 0 23 Oct 2018
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Roman Novak Lechao Xiao Jaehoon Lee Yasaman Bahri Greg Yang Jiri Hron Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 54 308 0 11 Oct 2018
Deep Convolutional Networks as shallow Gaussian Processes Adrià Garriga-Alonso C. Rasmussen Laurence Aitchison BDL UQCV 81 270 0 16 Aug 2018
Geometry of energy landscapes and the optimizability of deep neural networks Simon Becker Yao Zhang A. Lee 27 30 0 01 Aug 2018
Comparing Dynamics: Deep Neural Networks versus Glassy Systems Marco Baity-Jesi Levent Sagun Mario Geiger S. Spigler Gerard Ben Arous C. Cammarota Yann LeCun Matthieu Wyart Giulio Biroli AI4CE 89 114 0 19 Mar 2018
Neural Network Renormalization Group Shuo-Hui Li Lei Wang BDL DRL 57 125 0 08 Feb 2018
Deep linear neural networks with arbitrary loss: All local minima are global T. Laurent J. V. Brecht ODL 49 136 0 05 Dec 2017
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 113 1,091 0 01 Nov 2017
Classification and Geometry of General Perceptual Manifolds SueYeon Chung Daniel D. Lee H. Sompolinsky 52 154 0 17 Oct 2017
High-dimensional dynamics of generalization error in neural networks Madhu S. Advani Andrew M. Saxe AI4CE 128 469 0 10 Oct 2017
Convolutional Gaussian Processes Mark van der Wilk C. Rasmussen J. Hensman BDL 64 132 0 06 Sep 2017
Depth Creates No Bad Local Minima Haihao Lu Kenji Kawaguchi ODL FAtt 71 121 0 27 Feb 2017
Random Feature Expansions for Deep Gaussian Processes Kurt Cutajar Edwin V. Bonilla Pietro Michiardi Maurizio Filippone BDL 42 144 0 14 Oct 2016
Learning to Communicate with Deep Multi-Agent Reinforcement Learning Jakob N. Foerster Yannis Assael Nando de Freitas Shimon Whiteson 142 1,605 0 21 May 2016
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 98 944 0 18 Feb 2015
Statistical mechanics of complex neural systems and high dimensional data Madhu S. Advani Subhaneil Lahiri Surya Ganguli AI4CE 67 70 0 30 Jan 2013