Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective

12 June 2019

Papers citing "Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective"

9 / 9 papers shown

Title
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions S. Bhattamishra Arkil Patel Varun Kanade Phil Blunsom 22 44 0 22 Nov 2022
Separation of Scales and a Thermodynamic Description of Feature Learning in Some CNNs Inbar Seroussi Gadi Naveh Zohar Ringel 33 50 0 31 Dec 2021
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs Gadi Naveh Zohar Ringel SSL MLT 36 31 0 08 Jun 2021
Explaining Neural Scaling Laws Yasaman Bahri Ethan Dyer Jared Kaplan Jaehoon Lee Utkarsh Sharma 27 250 0 12 Feb 2021
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders Yibo Jiang Cengiz Pehlevan 19 13 0 30 Jun 2020
Predicting the outputs of finite deep neural networks trained with noisy gradients Gadi Naveh Oded Ben-David H. Sompolinsky Zohar Ringel 16 20 0 02 Apr 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 146 201 0 07 Feb 2020
Non-Gaussian processes and neural networks at finite widths Sho Yaida 12 88 0 30 Sep 2019
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 183 1,185 0 30 Nov 2014