Fetching the paper…
Reading the bibliography…
Constraining linear layers in neural networks to respect symmetry transformations from a group $G$ is a common design principle for invariant networks that has found many applications in machine learning.
Permutation groups through invariant relations and invariant functions
Wielandt, H · 1969
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D · 1989
Earlier work this paper cites.
Cohen, T. S. and Welling, M · 1990
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K · 1991
Earlier work this paper cites.
Automorphism groups, isomorphism, reconstruction. chapter 27 of the handbook of combinatorics, 1447–1540. rl graham, m. grötschel, l. lovász eds, 1995
Babai, L · 1995
Earlier work this paper cites.
Computing bases for rings of permutation-invariant polynomials
Göbel, M · 1995
Earlier work this paper cites.
Permutation groups , volume 163
Dixon, J. D. and Mortimer, B · 1996
Earlier work this paper cites.
Classical invariant theory, a primer
Kraft, H. and Procesi, C · 2000
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
Cited alongside, same era.
Equivariance through parameter-sharing
Ravanbakhsh, S., Schneider, J., and Poczos, B · 2017
Cited alongside, same era.
Deep models of interactions across sets
Hartford, J., Graham, D. R., Leyton-Brown, K., and Ravanbakhsh, S · 2018
Later among the works it cites.
Kondor, R. and Trivedi, S · 2018
Later among the works it cites.
Covariant compositional networks for learning graphs
Kondor, R., Son, H. T., Pan, H., Anderson, B., and Trivedi, S · 2018
Later among the works it cites.
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Weiler, M., Geiger, M., Welling, M., Boomsma, W., and Cohen, T · 2018
Later among the works it cites.
Universal approximations of invariant maps by neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
Cited alongside, same era.
Cohen, T. S., Geiger, M., Köhler, J., and Welling, M · 2018
Cited alongside, same era.
Group equivariant convolutional networks
Cohen, T. and Welling, M
Cited in the paper.
Yarotsky, D · 2018
Later among the works it cites.
Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2019
Closest in time.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Closest in time.