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Approximation-Generalization Trade-offs under (Approximate) Group Equivariance

27 May 2023

Papers citing "Approximation-Generalization Trade-offs under (Approximate) Group Equivariance"

50 / 52 papers shown

Title
Learning (Approximately) Equivariant Networks via Constrained Optimization Andrei Manolache Luiz F.O. Chamon Mathias Niepert 126 0 0 19 May 2025
AtlasD: Automatic Local Symmetry Discovery Manu Bhat Jonghyun Park Jianke Yang Nima Dehmamy Robin Walters Rose Yu 65 0 0 15 Apr 2025
PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL Joshua McClellan Greyson Brothers Furong Huang Pratap Tokekar 85 0 0 19 Mar 2025
Learning with Exact Invariances in Polynomial Time Ashkan Soleymani B. Tahmasebi Stefanie Jegelka Patrick Jaillet 248 0 0 27 Feb 2025
Approximate Equivariance in Reinforcement Learning Jung Yeon Park Sujay Bhatt Sihan Zeng Lawson L. S. Wong Alec Koppel Sumitra Ganesh Robin Walters 96 1 0 06 Nov 2024
Does equivariance matter at scale? Johann Brehmer S. Behrends P. D. Haan Taco S. Cohen 106 15 0 30 Oct 2024
Relaxed Equivariance via Multitask Learning Ahmed A. A. Elhag T. Konstantin Rusch Francesco Di Giovanni Michael Bronstein 118 3 0 23 Oct 2024
Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks Ashwin Samudre Mircea Petrache Brian D. Nord Shubhendu Trivedi 151 2 0 18 Sep 2024
Improving Equivariant Model Training via Constraint Relaxation Stefanos Pertigkiozoglou Evangelos Chatzipantazis Shubhendu Trivedi Kostas Daniilidis 106 6 0 23 Aug 2024
Approximately Equivariant Neural Processes Matthew Ashman Cristiana-Diana Diaconu Adrian Weller W. Bruinsma Richard E. Turner BDL 91 1 0 19 Jun 2024
Learning equivariant tensor functions with applications to sparse vector recovery Wilson Gregory Josué Tonelli-Cueto Nicholas F. Marshall Andrew S. Lee Soledad Villar 89 2 0 03 Jun 2024
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters Ya-Wei Eileen Lin Ronen Talmon Ron Levie 87 0 0 03 Jun 2024
Symmetries in Overparametrized Neural Networks: A Mean-Field View Javier Maass Joaquin Fontbona MLT FedML 177 2 0 30 May 2024
Almost Equivariance via Lie Algebra Convolutions Daniel McNeela 132 7 0 19 Oct 2023
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution Rui Wang E. Hofgard Han Gao Robin Walters Tess E. Smidt AI4CE 141 12 0 03 Oct 2023
Approximately Equivariant Graph Networks Ningyuan Huang Ron Levie Soledad Villar 111 19 0 21 Aug 2023
The Exact Sample Complexity Gain from Invariances for Kernel Regression B. Tahmasebi Stefanie Jegelka 63 19 0 24 Mar 2023
Attending to Graph Transformers Luis Muller Mikhail Galkin Christopher Morris Ladislav Rampášek 104 93 0 08 Feb 2023
Towards fully covariant machine learning Soledad Villar D. Hogg Weichi Yao George A. Kevrekidis Bernhard Schölkopf AI4CE 106 11 0 31 Jan 2023
A PAC-Bayesian Generalization Bound for Equivariant Networks Arash Behboodi Gabriele Cesa Taco S. Cohen 91 19 0 24 Oct 2022
Equivariant Transduction through Invariant Alignment Jennifer C. White Ryan Cotterell 81 4 0 22 Sep 2022
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces Yinshuang Xu Jiahui Lei Yan Sun Kostas Daniilidis 88 20 0 16 Jun 2022
Relaxing Equivariance Constraints with Non-stationary Continuous Filters Tycho F. A. van der Ouderaa David W. Romero Mark van der Wilk 88 37 0 14 Apr 2022
Sample Efficient Grasp Learning Using Equivariant Models Xu Zhu Dian Wang Ondrej Biza Guanang Su Robin Walters Robert Platt DRL 151 70 0 18 Feb 2022
A Theory of PAC Learnability under Transformation Invariances Hang Shao Omar Montasser Avrim Blum 80 21 0 15 Feb 2022
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics Rui Wang Robin Walters Rose Yu 127 82 0 28 Jan 2022
Residual Pathway Priors for Soft Equivariance Constraints Marc Finzi Gregory W. Benton A. Wilson BDL UQCV 86 59 0 02 Dec 2021
Understanding the Generalization Benefit of Model Invariance from a Data Perspective Sicheng Zhu Bang An Furong Huang 51 26 0 10 Nov 2021
Learning Partial Equivariances from Data David W. Romero Suhas Lohit 73 32 0 19 Oct 2021
Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds Maurice Weiler Patrick Forré E. Verlinde Max Welling 64 84 0 10 Jun 2021
Provably Strict Generalisation Benefit for Invariance in Kernel Methods Bryn Elesedy 88 27 0 04 Jun 2021
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups Marc Finzi Max Welling A. Wilson 169 198 0 19 Apr 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 107 91 0 25 Feb 2021
Provably Strict Generalisation Benefit for Equivariant Models Bryn Elesedy Sheheryar Zaidi AI4CE 96 88 0 20 Feb 2021
Truly shift-invariant convolutional neural networks Anadi Chaman Ivan Dokmanić 109 71 0 28 Nov 2020
Scale Equivariance Improves Siamese Tracking Ivan Sosnovik A. Moskalev A. Smeulders 50 79 0 17 Jul 2020
On the Benefits of Invariance in Neural Networks Clare Lyle Mark van der Wilk Marta Z. Kwiatkowska Y. Gal Benjamin Bloem-Reddy OOD BDL 84 96 0 01 May 2020
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location O. Kayhan Jan van Gemert 339 237 0 16 Mar 2020
Directional Message Passing for Molecular Graphs Johannes Klicpera Janek Groß Stephan Günnemann 151 881 0 06 Mar 2020
Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces Akiyoshi Sannai Masaaki Imaizumi M. Kawano MLT 55 31 0 15 Oct 2019
B-Spline CNNs on Lie Groups Erik J. Bekkers AI4CE 103 131 0 26 Sep 2019
Cormorant: Covariant Molecular Neural Networks Brandon M. Anderson Truong-Son Hy Risi Kondor 139 426 0 06 Jun 2019
Gauge Equivariant Convolutional Networks and the Icosahedral CNN Taco S. Cohen Maurice Weiler Berkay Kicanaoglu Max Welling 139 414 0 11 Feb 2019
Invariant and Equivariant Graph Networks Haggai Maron Heli Ben-Hamu Nadav Shamir Y. Lipman 176 508 0 24 Dec 2018
A General Theory of Equivariant CNNs on Homogeneous Spaces Taco S. Cohen Mario Geiger Maurice Weiler MLT AI4CE 253 316 0 05 Nov 2018
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups Risi Kondor Shubhendu Trivedi MLT 173 500 0 11 Feb 2018
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur Srinadh Bhojanapalli Nathan Srebro 94 610 0 29 Jul 2017
Equivariance Through Parameter-Sharing Siamak Ravanbakhsh J. Schneider Barnabás Póczós 120 258 0 27 Feb 2017
Generalization Error of Invariant Classifiers Jure Sokolić Raja Giryes Guillermo Sapiro M. Rodrigues 74 78 0 14 Oct 2016
Group Equivariant Convolutional Networks Taco S. Cohen Max Welling BDL 215 1,949 0 24 Feb 2016