Asymptotics of representation learning in finite Bayesian neural networks

1 June 2021

Jacob A. Zavatone-Veth

Papers citing "Asymptotics of representation learning in finite Bayesian neural networks"

20 / 20 papers shown

Title
Using Autodiff to Estimate Posterior Moments, Marginals and Samples Sam Bowyer Thomas Heap Laurence Aitchison 43 1 0 26 Oct 2023
Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit Blake Bordelon Lorenzo Noci Mufan Li Boris Hanin Cengiz Pehlevan 35 22 0 28 Sep 2023
A theory of data variability in Neural Network Bayesian inference Javed Lindner David Dahmen Michael Krämer M. Helias BDL 32 1 0 31 Jul 2023
Neural Network Field Theories: Non-Gaussianity, Actions, and Locality M. Demirtaş James Halverson Anindita Maiti M. Schwartz Keegan Stoner AI4CE 23 10 0 06 Jul 2023
Structures of Neural Network Effective Theories cCaugin Ararat Tianji Cai Cem Tekin Zhengkang Zhang 60 7 0 03 May 2023
Learning curves for deep structured Gaussian feature models Jacob A. Zavatone-Veth Cengiz Pehlevan MLT 30 11 0 01 Mar 2023
Neural networks learn to magnify areas near decision boundaries Jacob A. Zavatone-Veth Sheng Yang Julian Rubinfien Cengiz Pehlevan MLT AI4CE 30 6 0 26 Jan 2023
The Curious Case of Benign Memorization Sotiris Anagnostidis Gregor Bachmann Lorenzo Noci Thomas Hofmann AAML 54 8 0 25 Oct 2022
The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization Mufan Li Mihai Nica Daniel M. Roy 53 37 0 06 Jun 2022
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks Blake Bordelon Cengiz Pehlevan MLT 45 77 0 19 May 2022
Contrasting random and learned features in deep Bayesian linear regression Jacob A. Zavatone-Veth William L. Tong Cengiz Pehlevan BDL MLT 30 27 0 01 Mar 2022
On neural network kernels and the storage capacity problem Jacob A. Zavatone-Veth Cengiz Pehlevan 11 6 0 12 Jan 2022
Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks Shaun Li AI4CE 46 0 0 03 Jan 2022
Separation of Scales and a Thermodynamic Description of Feature Learning in Some CNNs Inbar Seroussi Gadi Naveh Zohar Ringel 40 51 0 31 Dec 2021
Depth induces scale-averaging in overparameterized linear Bayesian neural networks Jacob A. Zavatone-Veth Cengiz Pehlevan BDL UQCV MDE 41 9 0 23 Nov 2021
The edge of chaos: quantum field theory and deep neural networks Kevin T. Grosvenor R. Jefferson 40 22 0 27 Sep 2021
A theory of representation learning gives a deep generalisation of kernel methods Adam X. Yang Maxime Robeyns Edward Milsom Ben Anson Nandi Schoots Laurence Aitchison BDL 32 10 0 30 Aug 2021
The Low-Rank Simplicity Bias in Deep Networks Minyoung Huh H. Mobahi Richard Y. Zhang Brian Cheung Pulkit Agrawal Phillip Isola 35 110 0 18 Mar 2021
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison 175 51 0 17 Oct 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 244 350 0 14 Jun 2018