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Equivariances provide useful inductive biases in neural network modeling, with the translation equivariance of convolutional neural networks being a canonical example.
Theory of nonstationary linear filtering in the fourier domain with application to time-variant filtering
Gary F Margrave · 1998
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Random features for large-scale kernel machines
Ali Rahimi, Benjamin Recht, et al · 2007
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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On the error of random fourier features
Danica J Sutherland and Jeff Schneider · 2015
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Harmonic networks: Deep translation and rotation equivariance
Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow · 2017
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Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, and Alexander Smola · 2017
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Roto-translation covariant convolutional networks for medical image analysis
Erik J Bekkers, Maxime W Lafarge, Mitko Veta, Koen AJ Eppenhof, Josien PW Pluim, and Remco Duits · 2018
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Taco S Cohen, Mario Geiger, Jonas Köhler, and Max Welling · 2018
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On the generalization of equivariance and convolution in neural networks to the action of compact groups
Risi Kondor and Shubhendu Trivedi · 2018
Cited alongside, same era.
Clebsch–gordan nets: a fully fourier space spherical convolutional neural network
Risi Kondor, Zhen Lin, and Shubhendu Trivedi · 2018
Cited alongside, same era.
An intriguing failing of convolutional neural networks and the coordconv solution
Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski · 2018
Cited alongside, same era.
Learning invariances using the marginal likelihood
Mark van der Wilk, Matthias Bauer, ST John, and James Hensman · 2018
Cited alongside, same era.
3d steerable cnns: Learning rotationally equivariant features in volumetric data
Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T Barron, and Ren Ng · 2020
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Meta-learning symmetries by reparameterization
Allan Zhou, Tom Knowles, and Chelsea Finn · 2020
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Noether networks: meta-learning useful conserved quantities
Ferran Alet, Dylan Doblar, Allan Zhou, Josh Tenenbaum, Kenji Kawaguchi, and Chelsea Finn · 2021
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Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen · 2018
Cited alongside, same era.
On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
Cited alongside, same era.
Deep scale-spaces: Equivariance over scale
Daniel E Worrall and Max Welling · 2019
Cited alongside, same era.
Frequency bias in neural networks for input of non-uniform density
Ronen Basri, Meirav Galun, Amnon Geifman, David Jacobs, Yoni Kasten, and Shira Kritchman · 2020
Cited alongside, same era.
Learning invariances in neural networks
Gregory Benton, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2020
Cited alongside, same era.
Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson · 2020
Cited alongside, same era.
Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
Cited alongside, same era.
Optimizing millions of hyperparameters by implicit differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
Cited alongside, same era.
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Learning equivariances and partial equivariances from data
David W Romero and Suhas Lohit · 2021
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E (n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Last layer marginal likelihood for invariance learning
Pola Schwöbel, Martin Jørgensen, Sebastian W Ober, and Mark van der Wilk · 2021
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Learning invariant weights in neural networks
Tycho FA van der Ouderaa and Mark van der Wilk · 2021
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Invariance learning in deep neural networks with differentiable laplace approximations, 2022
Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, and Mark van der Wilk · 2022
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Exploiting redundancy: Separable group convolutional networks on lie groups
David M Knigge, David W Romero, and Erik J Bekkers · 2022
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Approximately equivariant networks for imperfectly symmetric dynamics
Rui Wang, Robin Walters, and Rose Yu · 2022
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