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Equivariant neural networks require explicit knowledge of the symmetry group.
An extension of tietze’s theorem
Dugundji, J · 1951
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Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., and White, H · 1989
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Transforming auto-encoders
Hinton, G. E., Krizhevsky, A., and Wang, S. D · 2011
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Smooth manifolds
Lee, J. M. and Lee, J. M · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
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3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J · 2015
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Brunton, S. L., Proctor, J. L., and Kutz, J. N · 2016
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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Cohen, T. S., Geiger, M., Köhler, J., and Welling, M · 2018
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Explorations in homeomorphic variational auto-encoding
Falorsi, L., De Haan, P., Davidson, T. R., De Cao, N., Weiler, M., Forré, P., and Cohen, T. S · 2018
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Towards a definition of disentangled representations
Higgins, I., Amos, D., Pfau, D., Racaniere, S., Matthey, L., Rezende, D., and Lerchner, A · 2018
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On the generalization of equivariance and convolution in neural networks to the action of compact groups
Kondor, R. and Trivedi, S · 2018
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Bekkers, E. J · 2019
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Symmetry-based disentangled representation learning requires interaction with environments
Caselles-Dupré, H., Garcia Ortiz, M., and Filliat, D · 2019
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Data-driven discovery of coordinates and governing equations
Champion, K., Lusch, B., Kutz, J. N., and Brunton, S. L · 2019
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The machine learning landscape of top taggers
Kasieczka, G., Plehn, T., Butter, A., Cranmer, K., Debnath, D., Dillon, B. M., Fairbairn, M., Faroughy, D. A., Fedorko, W., Gay, C., et al · 2019
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General e (2)-equivariant steerable cnns
Weiler, M. and Cesa, G · 2019
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Learning invariances in neural networks from training data
Benton, G., Finzi, M., Izmailov, P., and Wilson, A. G · 2020
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Finzi, M., Stanton, S., Izmailov, P., and Wilson, A. G · 2020
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Sindy-pi: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics
Meta-learning symmetries by reparameterization
Zhou, A., Knowles, T., and Finn, C · 2021
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Symmetry discovery with deep learning
Desai, K., Nachman, B., and Thaler, J · 2022
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Ensemble-sindy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
Fasel, U., Kutz, J. N., Brunton, B. W., and Brunton, S. L · 2022
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Liegg: Studying learned lie group generators
Moskalev, A., Sepliarskaia, A., Sosnovik, I., and Smeulders, A · 2022
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Learning symmetric embeddings for equivariant world models
Park, J. Y., Biza, O., Zhao, L., van de Meent, J. W., and Walters, R · 2022
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Learning partial equivariances from data
Romero, D. W. and Lohit, S · 2022
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Kaheman, K., Kutz, J. N., and Brunton, S. L · 2020
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Learning disentangled representations and group structure of dynamical environments
Quessard, R., Barrett, T., and Clements, W · 2020
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Inverse learning of symmetries
Wieser, M., Parbhoo, S., Wieczorek, A., and Roth, V · 2020
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Bronstein, M. M., Bruna, J., Cohen, T., and Veličković, P · 2021
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Learning augmentation distributions using transformed risk minimization
Chatzipantazis, E., Pertigkiozoglou, S., Dobriban, E., and Daniilidis, K · 2021
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Equivariant contrastive learning
Dangovski, R., Jing, L., Loh, C., Han, S., Srivastava, A., Cheung, B., Agrawal, P., and Soljačić, M · 2021
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Automatic symmetry discovery with lie algebra convolutional network
Dehmamy, N., Walters, R., Liu, Y., Wang, D., and Yu, R · 2021
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Structuring representations using group invariants
Shakerinava, M., Mondal, A. K., and Ravanbakhsh, S · 2022
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Unsupervised learning of group invariant and equivariant representations
Winter, R., Bertolini, M., Le, T., Noe, F., and Clevert, D.-A · 2022
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Rotationally equivariant 3d object detection
Yu, H.-X., Wu, J., and Yi, L · 2022
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Self-supervised learning of split invariant equivariant representations
Garrido, Q., Najman, L., and Lecun, Y · 2023
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A unified framework for discovering discrete symmetries
Karjol, P., Kashyap, R., Gopalan, A., et al · 2023
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Equivariant representation learning via class-pose decomposition
Marchetti, G. L., Tegnér, G., Varava, A., and Kragic, D · 2023
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On genuine invariance learning without weight-tying, 2023
Moskalev, A., Sepliarskaia, A., Bekkers, E. J., and Smeulders, A · 2023
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Generative adversarial symmetry discovery
Yang, J., Walters, R., Dehmamy, N., and Yu, R · 2023
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