Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures

13 December 2022

Papers citing "Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures"

9 / 9 papers shown

Title
Towards understanding epoch-wise double descent in two-layer linear neural networks Amanda Olmin Fredrik Lindsten MLT 27 3 0 13 Jul 2024
Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noise Jean Barbier Francesco Camilli Marco Mondelli Yizhou Xu 25 2 0 31 May 2024
Grokking in Linear Estimators -- A Solvable Model that Groks without Understanding Noam Levi Alon Beck Yohai Bar-Sinai 26 16 0 25 Oct 2023
Gradient flow on extensive-rank positive semi-definite matrix denoising A. Bodin N. Macris 28 3 0 16 Mar 2023
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression Lechao Xiao Hong Hu Theodor Misiakiewicz Yue M. Lu Jeffrey Pennington 59 18 0 30 May 2022
Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime Hong Hu Yue M. Lu 51 15 0 13 May 2022
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime Stéphane dÁscoli Maria Refinetti Giulio Biroli Florent Krzakala 93 152 0 02 Mar 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar C. Pehlevan 136 201 0 07 Feb 2020
Cleaning large correlation matrices: tools from random matrix theory J. Bun J. Bouchaud M. Potters 29 262 0 25 Oct 2016