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Group Convolutional Neural Networks (G-CNNs) constrain learned features to respect the symmetries in the selected group, and lead to better generalization when these symmetries appear in the data.
Gradient-based learning applied to document recognition
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Wavelet networks: Scale equivariant learning from raw waveforms
D. W. Romero, E. J. Bekkers, J. M. Tomczak, and M. Hoogendoorn · 2006
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An empirical evaluation of deep architectures on problems with many factors of variation
H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio · 2007
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Learning multiple layers of features from tiny images
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Group equivariant convolutional networks
T. Cohen and M. Welling · 2016
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Exploiting cyclic symmetry in convolutional neural networks
S. Dieleman, J. De Fauw, and K. Kavukcuoglu · 2016
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Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 2016
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Temporal ensembling for semi-supervised learning
S. Laine and T. Aila · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
C. J. Maddison, A. Mnih, and Y. W. Teh · 2016
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
B. E. Bejnordi, M. Veta, P. J. Van Diest, B. Van Ginneken, N. Karssemeijer, G. Litjens, J. A. Van Der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, et al · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
K. T. Schütt, P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, and K.-R. Müller · 2017
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Harmonic networks: Deep translation and rotation equivariance
D. E. Worrall, S. J. Garbin, D. Turmukhambetov, and G. J. Brostow · 2017
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Spherical cnns
T. S. Cohen, M. Geiger, J. Köhler, and M. Welling · 2018
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Learning invariances using the marginal likelihood
M. van der Wilk, M. Bauer, S. John, and J. Hensman · 2018
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Rotation equivariant cnns for digital pathology
B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, and M. Welling · 2018
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A general theory of equivariant cnns on homogeneous spaces
T. S. Cohen, M. Geiger, and M. Weiler · 2019
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Reparameterizing distributions on lie groups
L. Falorsi, P. de Haan, T. R. Davidson, and P. Forré · 2019
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Integer discrete flows and lossless compression
E. Hoogeboom, J. Peters, R. Van Den Berg, and M. Welling · 2019
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Fast autoaugment
S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim · 2019
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Faster autoaugment: Learning augmentation strategies using backpropagation
R. Hataya, J. Zdenek, K. Yoshizoe, and H. Nakayama · 2020
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DADA: differentiable automatic data augmentation
Y. Li, G. Hu, Y. Wang, T. M. Hospedales, N. M. Robertson, and Y. Yang · 2020
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Group equivariant stand-alone self-attention for vision
D. W. Romero and J.-B. Cordonnier · 2020
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Implicit neural representations with periodic activation functions
V. Sitzmann, J. Martel, A. Bergman, D. Lindell, and G. Wetzstein · 2020
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Scale-equivariant steerable networks
I. Sosnovik, M. Szmaja, and A. Smeulders · 2020
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Meta-learning symmetries by reparameterization
A. Zhou, T. Knowles, and C. Finn · 2020
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General e (2)-equivariant steerable cnns
M. Weiler and G. Cesa · 2019
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Deep scale-spaces: Equivariance over scale
D. Worrall and M. Welling · 2019
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Learning invariances in neural networks from training data
G. Benton, M. Finzi, P. Izmailov, and A. G. Wilson · 2020
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Lorentz group equivariant neural network for particle physics
A. Bogatskiy, B. Anderson, J. Offermann, M. Roussi, D. Miller, and R. Kondor · 2020
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A group-theoretic framework for data augmentation
S. Chen, E. Dobriban, and J. H. Lee · 2020
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Spin-weighted spherical cnns
C. Esteves, A. Makadia, and K. Daniilidis · 2020
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Noether networks: meta-learning useful conserved quantities
F. Alet, D. Doblar, A. Zhou, J. Tenenbaum, K. Kawaguchi, and C. Finn · 2021
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Learning augmentation distributions using transformed risk minimization
E. Chatzipantazis, S. Pertigkiozoglou, E. Dobriban, and K. Daniilidis · 2021
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Automatic symmetry discovery with lie algebra convolutional network
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Argmax flows and multinomial diffusion: Towards non-autoregressive language models
E. Hoogeboom, D. Nielsen, P. Jaini, P. Forré, and M. Welling · 2021
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Lietransformer: equivariant self-attention for lie groups
M. J. Hutchinson, C. Le Lan, S. Zaidi, E. Dupont, Y. W. Teh, and H. Kim · 2021
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Exploiting learned symmetries in group equivariant convolutions
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Learning invariant weights in neural networks
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Exploiting redundancy: Separable group convolutional networks on lie groups
D. M. Knigge, D. W. Romero, and E. J. Bekkers · 2022
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