A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs

8 June 2021

Papers citing "A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs"

11 / 11 papers shown

Title
Deep Neural Nets as Hamiltonians Mike Winer Boris Hanin 112 0 0 31 Mar 2025
Bayesian RG Flow in Neural Network Field Theories Jessica N. Howard Marc S. Klinger Anindita Maiti A. G. Stapleton 65 1 0 27 May 2024
Restoring balance: principled under/oversampling of data for optimal classification Emanuele Loffredo Mauro Pastore Simona Cocco R. Monasson 35 9 0 15 May 2024
Quantitative CLTs in Deep Neural Networks Stefano Favaro Boris Hanin Domenico Marinucci I. Nourdin G. Peccati BDL 23 11 0 12 Jul 2023
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks Blake Bordelon C. Pehlevan MLT 38 29 0 06 Apr 2023
Globally Gated Deep Linear Networks Qianyi Li H. Sompolinsky AI4CE 14 10 0 31 Oct 2022
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks Blake Bordelon C. Pehlevan MLT 24 79 0 19 May 2022
Contrasting random and learned features in deep Bayesian linear regression Jacob A. Zavatone-Veth William L. Tong C. Pehlevan BDL MLT 28 26 0 01 Mar 2022
Separation of Scales and a Thermodynamic Description of Feature Learning in Some CNNs Inbar Seroussi Gadi Naveh Z. Ringel 25 49 0 31 Dec 2021
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar C. Pehlevan 131 200 0 07 Feb 2020
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison 173 51 0 17 Oct 2019