Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks

10 December 2021

Papers citing "Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks"

7 / 7 papers shown

Title
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models Song Mei 3DV AI4CE DiffM 31 11 0 29 Apr 2024
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression Lechao Xiao Hong Hu Theodor Misiakiewicz Yue M. Lu Jeffrey Pennington 51 18 0 30 May 2022
Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime Hong Hu Yue M. Lu 44 15 0 13 May 2022
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 44 89 0 25 Feb 2021
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 226 4,424 0 23 Jan 2020
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 220 347 0 14 Jun 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,231 0 24 Jun 2017