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Inspired by constraints from physical law, equivariant machine learning restricts the learning to a hypothesis class where all the functions are equivariant with respect to some group action.
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S. Ravanbakhsh, J. Schneider, and B. Poczos · 2017
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C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2017
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Neural ordinary differential equations
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H. Maron, H. Ben-Hamu, N. Shamir, and Y. Lipman · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Learning with invariances in random features and kernel models
S. Mei, T. Misiakiewicz, and A. Montanari · 2021
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E (n) equivariant graph neural networks
V. G. Satorras, E. Hoogeboom, and M. Welling · 2021
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Scalars are universal: Equivariant machine learning, structured like classical physics
S. Villar, D. W. Hogg, K. Storey-Fisher, W. Yao, and B. Blum-Smith · 2021
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Incorporating symmetry into deep dynamics models for improved generalization
R. Wang, R. Walters, and R. Yu · 2021
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N. Thomas, T. Smidt, S. Kearnes, L. Yang, L. Li, K. Kohlhoff, and P. Riley · 2018
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Universal approximations of invariant maps by neural networks
D. Yarotsky · 2018
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On the equivalence between graph isomorphism testing and function approximation with gnns
Z. Chen, S. Villar, L. Chen, and J. Bruna · 2019
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Gauge equivariant convolutional networks and the icosahedral cnn
T. Cohen, M. Weiler, B. Kicanaoglu, and M. Welling · 2019
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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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On the universality of invariant networks
H. Maron, E. Fetaya, N. Segol, and Y. Lipman · 2019
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Hamiltonian graph networks with ODE integrators
A. Sanchez-Gonzalez, V. Bapst, K. Cranmer, and P. W. Battaglia · 2019
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M. Weiler, P. Forré, E. Verlinde, and M. Welling · 2021
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A simple equivariant machine learning method for dynamics based on scalars, 2021
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Physics-guided ai for large-scale spatiotemporal data
R. Yu, P. Perdikaris, and A. Karpatne · 2021
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Dimensionally consistent learning with buckingham pi
J. Bakarji, J. Callaham, S. L. Brunton, and J. N. Kutz · 2022
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
S. Batzner, A. Musaelian, L. Sun, M. Geiger, J. P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky · 2022
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e3nn: Euclidean neural networks
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Equivariance versus augmentation for spherical images
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Sign and basis invariant networks for spectral graph representation learning
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Dimensionless machine learning: Imposing exact units equivariance
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Approximately equivariant networks for imperfectly symmetric dynamics
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Data augmentation vs. equivariant networks: A theory of generalization on dynamics forecasting
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Universal approximations of invariant maps by neural networks
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Invariants of finite and discrete group actions via moving frames
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The passive symmetries of machine learning
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