Gradient Descent as a Shrinkage Operator for Spectral Bias

25 April 2025

Papers citing "Gradient Descent as a Shrinkage Operator for Spectral Bias"

11 / 11 papers shown

Title
Trading Positional Complexity vs. Deepness in Coordinate Networks Jianqiao Zheng Sameera Ramasinghe Xueqian Li Simon Lucey 84 19 0 18 May 2022
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD Konstantinos E. Nikolakakis Farzin Haddadpour Amin Karbasi Dionysios S. Kalogerias 115 19 0 26 Apr 2022
Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPs Sameera Ramasinghe Simon Lucey 88 129 0 30 Nov 2021
Spectral Bias in Practice: The Role of Function Frequency in Generalization Sara Fridovich-Keil Raphael Gontijo-Lopes Rebecca Roelofs 82 30 0 06 Oct 2021
Stochastic Training is Not Necessary for Generalization Jonas Geiping Micah Goldblum Phillip E. Pope Michael Moeller Tom Goldstein 163 76 0 29 Sep 2021
Rethinking Positional Encoding Jianqiao Zheng Sameera Ramasinghe Simon Lucey 72 52 0 06 Jul 2021
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains Matthew Tancik Pratul P. Srinivasan B. Mildenhall Sara Fridovich-Keil N. Raghavan Utkarsh Singhal R. Ramamoorthi Jonathan T. Barron Ren Ng 135 2,449 0 18 Jun 2020
Implicit Neural Representations with Periodic Activation Functions Vincent Sitzmann Julien N. P. Martel Alexander W. Bergman David B. Lindell Gordon Wetzstein AI4TS 172 2,577 0 17 Jun 2020
Gradient Dynamics of Shallow Univariate ReLU Networks Francis Williams Matthew Trager Claudio Silva Daniele Panozzo Denis Zorin Joan Bruna 64 80 0 18 Jun 2019
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 351 4,636 0 10 Nov 2016
Optimal Shrinkage of Singular Values M. Gavish D. Donoho 118 182 0 29 May 2014