On Non-Linear operators for Geometric Deep Learning

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On Non-Linear operators for Geometric Deep Learning

6 July 2022

G. Sergeant-Perthuis

Joan Bruna

Edouard Oyallon

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Papers citing "On Non-Linear operators for Geometric Deep Learning"

14 / 14 papers shown

Title
Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds Maurice Weiler Patrick Forré E. Verlinde Max Welling 57 84 0 10 Jun 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 355 1,153 0 27 Apr 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 96 90 0 25 Feb 2021
Universal Approximation Theorem for Equivariant Maps by Group CNNs Wataru Kumagai Akiyoshi Sannai 83 14 0 27 Dec 2020
Machine Learning for Fluid Mechanics Steven Brunton B. R. Noack Petros Koumoutsakos AI4CE PINN 87 2,122 0 27 May 2019
Universal Invariant and Equivariant Graph Neural Networks Nicolas Keriven Gabriel Peyré 189 292 0 13 May 2019
Gauge Equivariant Convolutional Networks and the Icosahedral CNN Taco S. Cohen Maurice Weiler Berkay Kicanaoglu Max Welling 108 412 0 11 Feb 2019
Universal approximations of invariant maps by neural networks Dmitry Yarotsky 124 214 0 26 Apr 2018
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups Risi Kondor Shubhendu Trivedi MLT 112 500 0 11 Feb 2018
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 810 3,287 0 24 Nov 2016
Group Equivariant Convolutional Networks Taco S. Cohen Max Welling BDL 169 1,941 0 24 Feb 2016
Understanding Deep Convolutional Networks S. Mallat FAtt AI4CE 175 639 0 19 Jan 2016
Invariant Scattering Convolution Networks Joan Bruna S. Mallat 122 1,278 0 05 Mar 2012
Group Invariant Scattering S. Mallat 123 991 0 12 Jan 2011