On the Benefits of Invariance in Neural Networks

1 May 2020

Papers citing "On the Benefits of Invariance in Neural Networks"

24 / 24 papers shown

Title
Lie Group Symmetry Discovery and Enforcement Using Vector Fields Ben Shaw Sasidhar Kunapuli A. Magner Kevin R. Moon 27 0 0 13 May 2025
Data Augmentation and Regularization for Learning Group Equivariance Oskar Nordenfors Axel Flinth 57 0 0 10 Feb 2025
Relaxed Equivariance via Multitask Learning Ahmed A. A. Elhag T. Konstantin Rusch Francesco Di Giovanni Michael Bronstein 50 2 0 23 Oct 2024
Revisiting Random Walks for Learning on Graphs Jinwoo Kim Olga Zaghen Ayhan Suleymanzade Youngmin Ryou Seunghoon Hong 59 0 0 01 Jul 2024
Symmetries in Overparametrized Neural Networks: A Mean-Field View Javier Maass Martínez Joaquin Fontbona FedML MLT 50 2 0 30 May 2024
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers Haoyang Liu Aditya Singh Yijiang Li Haohan Wang AAML ViT 36 1 0 15 Mar 2024
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance Mircea Petrache Shubhendu Trivedi 35 22 0 27 May 2023
Optimization Dynamics of Equivariant and Augmented Neural Networks Axel Flinth F. Ohlsson 41 5 0 23 Mar 2023
Steerable Equivariant Representation Learning Sangnie Bhardwaj Willie McClinton Tongzhou Wang Guillaume Lajoie Chen Sun Phillip Isola Dilip Krishnan OOD LLMSV 31 5 0 22 Feb 2023
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization Sanae Lotfi Marc Finzi Sanyam Kapoor Andres Potapczynski Micah Goldblum A. Wilson BDL MLT AI4CE 29 51 0 24 Nov 2022
A PAC-Bayesian Generalization Bound for Equivariant Networks Arash Behboodi Gabriele Cesa Taco S. Cohen 53 17 0 24 Oct 2022
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making Tianyu Liu Jie Lu Zheng Yan Guangquan Zhang 36 0 0 16 Oct 2022
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? Henry Kvinge Tegan H. Emerson Grayson Jorgenson Scott Vasquez T. Doster Jesse D. Lew 43 8 0 07 Oct 2022
Augmentation Backdoors J. Rance Yiren Zhao Ilia Shumailov Robert D. Mullins AAML SILM 53 7 0 29 Sep 2022
Automatic Data Augmentation via Invariance-Constrained Learning Ignacio Hounie Luiz F. O. Chamon Alejandro Ribeiro 23 10 0 29 Sep 2022
On the Limitations of Stochastic Pre-processing Defenses Yue Gao Ilia Shumailov Kassem Fawaz Nicolas Papernot AAML SILM 36 30 0 19 Jun 2022
Testing for Geometric Invariance and Equivariance Louis Christie J. Aston 25 2 0 30 May 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 52 56 0 23 Feb 2022
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? M. Farrell Blake Bordelon Shubhendu Trivedi C. Pehlevan 18 5 0 14 Oct 2021
Implicit Bias of Linear Equivariant Networks Hannah Lawrence Kristian Georgiev A. Dienes B. Kiani AI4CE 37 14 0 12 Oct 2021
Reconstruction for Powerful Graph Representations Leonardo Cotta Christopher Morris Bruno Ribeiro AI4CE 130 78 0 01 Oct 2021
Data augmentation in Bayesian neural networks and the cold posterior effect Seth Nabarro Stoil Ganev Adrià Garriga-Alonso Vincent Fortuin Mark van der Wilk Laurence Aitchison BDL 21 37 0 10 Jun 2021
A General Theory of Equivariant CNNs on Homogeneous Spaces Taco S. Cohen Mario Geiger Maurice Weiler MLT AI4CE 165 308 0 05 Nov 2018
Pac-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning O. Catoni 148 454 0 03 Dec 2007