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Equivariant deep learning architectures exploit symmetries in learning problems to improve the sample efficiency of neural-network-based models and their ability to generalise.
Handwritten digit recognition with a back-propagation network
Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, Richard Howard, Wayne Hubbard, and Lawrence Jackel · 1989
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Bayesian Gaussian processes for regression and classification
Mark N Gibbs · 1998
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Functional analysis, Sobolev spaces and partial differential equations , volume 2
Haim Brezis · 2011
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Graph neural processes: Towards Bayesian graph neural networks
Andrew Carr and David Wingate · 2019
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Convolutional conditional neural processes
Jonathan Gordon, Wessel P Bruinsma, Andrew YK Foong, James Requeima, Yann Dubois, and Richard E Turner · 2019
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Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
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Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis, 2020
Copernicus Climate Change Service · 2020
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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.
Meta-learning stationary stochastic process prediction with convolutional neural processes
Andrew Foong, Wessel Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, and Richard Turner · 2020
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Learning to control pdes with differentiable physics
Philipp Holl, Nils Thuerey, and Vladlen Koltun · 2020
Cited alongside, same era.
Universal approximation theorem for equivariant maps by group CNNs
Wataru Kumagai and Akiyoshi Sannai · 2020
Cited alongside, same era.
Learning with invariances in random features and kernel models
Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2021
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Understanding the generalization benefit of model invariance from a data perspective
Sicheng Zhu, Bang An, and Furong Huang · 2021
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Convolutional Conditional Neural Processes
Wessel P. Bruinsma · 2022
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Latent bottlenecked attentive neural processes
Leo Feng, Hossein Hajimirsadeghi, Yoshua Bengio, and Mohamed Osama Ahmed · 2022
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Transformer neural processes: Uncertainty-aware meta learning via sequence modeling
Tung Nguyen and Aditya Grover · 2022
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Generalization capabilities of translationally equivariant neural networks
Srinath Bulusu, Matteo Favoni, Andreas Ipp, David I Müller, and Daniel Schuh · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Provably strict generalisation benefit for equivariant models
Bryn Elesedy and Sheheryar Zaidi · 2021
Cited alongside, same era.
Residual pathway priors for soft equivariance constraints
Marc Finzi, Gregory Benton, and Andrew G Wilson · 2021
Cited alongside, same era.
Equivariant learning of stochastic fields: Gaussian processes and steerable conditional neural processes
Peter Holderrieth, Michael J Hutchinson, and Yee Whye Teh · 2021
Cited alongside, same era.
Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira · 2021
Cited alongside, same era.
Group equivariant conditional neural processes
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, and Yutaka Matsuo · 2021
Cited alongside, same era.
David W Romero and Suhas Lohit · 2022
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Relaxing equivariance constraints with non-stationary continuous filters
Tycho van der Ouderaa, David W Romero, and Mark van der Wilk · 2022
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Practical equivariances via relational conditional neural processes
Daolang Huang, Manuel Haussmann, Ulpu Remes, ST John, Grégoire Clarté, Kevin Sebastian Luck, Samuel Kaski, and Luigi Acerbi · 2023
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Positional encodings as group representations: A unified framework
Derek Lim, Hannah Lawrence, Ningyuan Teresa Huang, and Erik Henning Thiede · 2023
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Approximation-generalization trade-offs under (approximate) group equivariance
Mircea Petrache and Shubhendu Trivedi · 2023
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Translation-equivariant transformer neural processes
Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P Bruinsma, and Richard E Turner · 2024
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