The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networks

8 October 2021

Papers citing "The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networks"

9 / 9 papers shown

Title
Infinite Width Limits of Self Supervised Neural Networks Maximilian Fleissner Gautham Govind Anil D. Ghoshdastidar SSL 115 0 0 17 Nov 2024
How Feature Learning Can Improve Neural Scaling Laws Blake Bordelon Alexander B. Atanasov C. Pehlevan 44 11 0 26 Sep 2024
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality Marko Medvedev Gal Vardi Nathan Srebro 35 3 0 05 Sep 2024
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting Neil Rohit Mallinar James B. Simon Amirhesam Abedsoltan Parthe Pandit M. Belkin Preetum Nakkiran 11 37 0 14 Jul 2022
Unsolved Problems in ML Safety Dan Hendrycks Nicholas Carlini John Schulman Jacob Steinhardt 164 268 0 28 Sep 2021
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics D. Kunin Javier Sagastuy-Breña Surya Ganguli Daniel L. K. Yamins Hidenori Tanaka 97 77 0 08 Dec 2020
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 150 232 0 04 Mar 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar C. Pehlevan 131 199 0 07 Feb 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 220 4,424 0 23 Jan 2020