On the non-universality of deep learning: quantifying the cost of symmetry

5 August 2022

Papers citing "On the non-universality of deep learning: quantifying the cost of symmetry"

6 / 6 papers shown

Title
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion Vinh Tong Trung-Dung Hoang Anji Liu Guy Van den Broeck Mathias Niepert DiffM 82 0 0 14 Feb 2025
Theoretical Analysis of Inductive Biases in Deep Convolutional Networks Zihao Wang Lei Wu 23 20 0 15 May 2023
A Mathematical Model for Curriculum Learning for Parities Elisabetta Cornacchia Elchanan Mossel 40 10 0 31 Jan 2023
On the Power of Differentiable Learning versus PAC and SQ Learning Emmanuel Abbe Pritish Kamath Eran Malach Colin Sandon Nathan Srebro MLT 77 23 0 09 Aug 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 176 1,111 0 27 Apr 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 46 89 0 25 Feb 2021