Learning curves for Gaussian process regression with power-law priors and targets

23 October 2021

Papers citing "Learning curves for Gaussian process regression with power-law priors and targets"

7 / 7 papers shown

Title
Breaking Neural Network Scaling Laws with Modularity Akhilan Boopathy Sunshine Jiang William Yue Jaedong Hwang Abhiram Iyer Ila Fiete OOD 62 2 0 09 Sep 2024
Characterizing the Spectrum of the NTK via a Power Series Expansion Michael Murray Hui Jin Benjamin Bowman Guido Montúfar 43 11 0 15 Nov 2022
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson Christopher van der Heide Fred Roosta Michael W. Mahoney UQCV 23 5 0 14 Oct 2022
Contrasting random and learned features in deep Bayesian linear regression Jacob A. Zavatone-Veth William L. Tong Cengiz Pehlevan BDL MLT 41 27 0 01 Mar 2022
Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions Maksim Velikanov Dmitry Yarotsky 24 6 0 02 Feb 2022
Universal scaling laws in the gradient descent training of neural networks Maksim Velikanov Dmitry Yarotsky 59 9 0 02 May 2021
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 149 201 0 07 Feb 2020