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Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds.
The qr transformation a unitary analogue to the lr transformation—part 1
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On the number of generators of a finite group
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Backpropagation applied to handwritten zip code recognition
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Representation theory and invariant neural networks
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Computing an eigenvector with inverse iteration
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Dense random finitely generated subgroups of lie groups
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Reliable krylov-based algorithms for matrix null space and rank
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A block wiedemann rank algorithm
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Generating sets
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Adam: A method for stochastic optimization
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Global convergence of stochastic gradient descent for some non-convex matrix problems
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Fast and simple pca via convex optimization
Garber, D. and Hazan, E · 2015
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A stochastic pca and svd algorithm with an exponential convergence rate
Shamir, O · 2015
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Esteves, C., Allen-Blanchette, C., Zhou, X., and Daniilidis, K · 2017
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Rotation equivariant vector field networks
Marcos, D., Volpi, M., Komodakis, N., and Tuia, D · 2017
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Searching for activation functions
Ramachandran, P., Zoph, B., and Le, Q. V · 2017
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Equivariance through parameter-sharing
Ravanbakhsh, S., Schneider, J., and Poczos, B · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
On the universality of invariant networks
Maron, H., Fetaya, E., Segol, N., and Lipman, Y · 2019
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General e (2)-equivariant steerable cnns
Weiler, M. and Cesa, G · 2019
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Deep scale-spaces: Equivariance over scale
Worrall, D. and Welling, M · 2019
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Lorentz group equivariant neural network for particle physics
Bogatskiy, A., Anderson, B., Offermann, J. T., Roussi, M., Miller, D. W., and Kondor, R · 2020
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On the universality of rotation equivariant point cloud networks
Dym, N. and Maron, H · 2020
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JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
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Neural ordinary differential equations
Chen, T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
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3d steerable cnns: Learning rotationally equivariant features in volumetric data
Weiler, M., Geiger, M., Welling, M., Boomsma, W., and Cohen, T. S · 2018
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Cormorant: Covariant molecular neural networks
Anderson, B., Hy, T. S., and Kondor, R · 2019
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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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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F. B., Worrall, D. E., Fischer, V., and Welling, M · 2020
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A wigner-eckart theorem for group equivariant convolution kernels
Lang, L. and Weiler, M · 2020
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On learning sets of symmetric elements
Maron, H., Litany, O., Chechik, G., and Fetaya, E · 2020
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Universal equivariant multilayer perceptrons
Ravanbakhsh, S · 2020
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Set2graph: Learning graphs from sets
Serviansky, H., Segol, N., Shlomi, J., Cranmer, K., Gross, E., Maron, H., and Lipman, Y · 2020
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github.com/e3nn/e3nn
Smidt, T., Geiger, M., and Rackers, J · 2020
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Mdp homomorphic networks: Group symmetries in reinforcement learning
van der Pol, E., Worrall, D., van Hoof, H., Oliehoek, F., and Welling, M · 2020
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Equivariant maps for hierarchical structures
Wang, R., Albooyeh, M., and Ravanbakhsh, S · 2020
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