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Equivariant neural networks have been widely used in a variety of applications due to their ability to generalize well in tasks where the underlying data symmetries are known.
Empirical processes: Theory and applications
Pollard, D · 1990
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ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., and Yu, F · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2016
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Machine learning of accurate energy-conserving molecular force fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., and Müller, K.-R · 2017
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Equivariance through parameter-sharing
Ravanbakhsh, S., Schneider, J. G., and Póczos, B · 2017
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Learning so (3) equivariant representations with spherical cnns
Esteves, C., Allen-Blanchette, C., Makadia, A., and Daniilidis, K · 2018
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Neural relational inference for interacting systems
Kipf, T., Fetaya, E., Wang, K.-C., Welling, M., and Zemel, R · 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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Clebsch–gordan nets: a fully fourier space spherical convolutional neural network
Kondor, R., Lin, Z., and Trivedi, S · 2018
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A general theory of equivariant CNNs on homogeneous spaces
Cohen, T. S., Geiger, M., and Weiler, M · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2019
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Dynamic graph cnn for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., and Solomon, J. M · 2019
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General E(2)-Equivariant Steerable CNNs
Weiler, M. and Cesa, G · 2019
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Pulmonary nodule detection in ct scans with equivariant cnns
Winkels, M. and Cohen, T · 2019
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B-spline cnns on lie groups
Bekkers, E. J · 2020
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Lorentz group equivariant neural network for particle physics
Bogatskiy, A., Anderson, B., Offermann, J., Roussi, M., Miller, D., and Kondor, R · 2020
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Permutation equivariant models for compositional generalization in language
Gordon, J., Lopez-Paz, D., Baroni, M., and Bouchacourt, D · 2020
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phiflow: A differentiable pde solving framework for deep learning via physical simulations
Holl, P. M., Um, K., and Thuerey, N · 2020
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Accurate prediction of protein structures and interactions using a three-track neural network
Baek, M., Dimaio, F., Anishchenko, I. V., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., Millán, C., Park, H., Adams, C., Glassman, C. R., DeGiovanni, A. M., Pereira, J. H., Rodrigues, A. V., van Dijk, A. A., Ebrecht, A. C., Opperman, D. J., Sagmeister, T., Buhlheller, C., Pavkov-Keller, T., Rathinaswamy, M. K., Dalwadi, U., Yip, C. K., Burke, J. E., Garcia, K. C., Grishin, N. V., Adams, P. D., Read, R. J., and Baker, D · 2021
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Sampling using su (n) gauge equivariant flows
Boyda, D., Kanwar, G., Racanière, S., Rezende, D. J., Albergo, M. S., Cranmer, K., Hackett, D. C., and Shanahan, P. E · 2021
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Geometric and physical quantities improve e(3) equivariant message passing
Brandstetter, J., Hesselink, R., van der Pol, E., Bekkers, E., and Welling, M · 2021
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Efficient generalized spherical {cnn}s
Cobb, O., Wallis, C. G. R., Mavor-Parker, A. N., Marignier, A., Price, M. A., d’Avezac, M., and McEwen, J · 2021
$\mathrm{SE}(3)$-equivariant attention networks for shape reconstruction in function space
Chatzipantazis, E., Pertigkiozoglou, S., Dobriban, E., and Daniilidis, K · 2023
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Optimization dynamics of equivariant and augmented neural networks, 2023
Flinth, A. and Ohlsson, F · 2023
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General framework for e (3)-equivariant neural network representation of density functional theory hamiltonian
Gong, X., Li, H., Zou, N., Xu, R., Duan, W., and Xu, Y · 2023
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The lie derivative for measuring learned equivariance
Gruver, N., Finzi, M. A., Goldblum, M., and Wilson, A. G · 2023
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Approximately equivariant graph networks
Huang, N., Levie, R., and Villar, S · 2023
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Vector neurons: a general framework for so(3)-equivariant networks
Deng, C., Litany, O., Duan, Y., Poulenard, A., Tagliasacchi, A., and Guibas, L · 2021
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Provably strict generalisation benefit for equivariant models
Elesedy, B. and Zaidi, S · 2021
Cited alongside, same era.
Residual pathway priors for soft equivariance constraints
Finzi, M., Benton, G., and Wilson, A. G · 2021
Cited alongside, same era.
Highly accurate protein structure prediction with alphafold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al · 2021
Cited alongside, same era.
E(n) equivariant normalizing flows for molecule generation in 3d
Satorras, V. G., Hoogeboom, E., Fuchs, F., Posner, I., and Welling, M · 2021
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Geometric deep learning of rna structure
Townshend, R. J. L., Eismann, S., Watkins, A. M., Rangan, R., Karelina, M., Das, R., and Dror, R. O · 2021
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Scalars are universal: Equivariant machine learning, structured like classical physics
Villar, S., Hogg, D. W., Storey-Fisher, K., Yao, W., and Blum-Smith, B · 2021
Cited alongside, same era.
Approximately-invariant neural networks for quantum many-body physics
Kufel, D., Kemp, J., and Yao, N. Y · 2023
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Liao, Y.-L. and Smidt, T · 2023
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Equivariance-aware architectural optimization of neural networks, 2023
Maile, K., Wilson, D. G., and Forré, P · 2023
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Equivariant adaptation of large pretrained models
Mondal, A. K., Panigrahi, S. S., Kaba, S.-O., Rajeswar, S., and Ravanbakhsh, S · 2023
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Scalable and equivariant spherical CNNs by discrete-continuous (DISCO) convolutions
Ocampo, J., Price, M. A., and McEwen, J · 2023
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Reducing so (3) convolutions to so (2) for efficient equivariant gnns
Passaro, S. and Zitnick, C. L · 2023
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Brauer’s group equivariant neural networks
Pearce-Crump, E · 2023
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Approximation-generalization trade-offs under (approximate) group equivariance
Petrache, M. and Trivedi, S · 2023
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Relaxed octahedral group convolution for learning symmetry breaking in 3d physical systems
Wang, R., Walters, R., and Smidt, T · 2023
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Enabling efficient equivariant operations in the fourier basis via gaunt tensor products
Luo, S., Chen, T., and Krishnapriyan, A. S · 2024
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Morphological symmetries in robotics
Ordoñez-Apraez, D., Turrisi, G., Kostic, V., Martin, M., Agudo, A., Moreno-Noguer, F., Pontil, M., Semini, C., and Mastalli, C · 2024
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A unified framework to enforce, discover, and promote symmetry in machine learning, 2024
Otto, S. E., Zolman, N., Kutz, J. N., and Brunton, S. L · 2024
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Petrache, M. and Trivedi, S · 2024
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Discovering symmetry breaking in physical systems with relaxed group convolution, 2024
Wang, R., Hofgard, E., Gao, H., Walters, R., and Smidt, T. E · 2024
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