Fetching the paper…
Reading the bibliography…
We present a simple proof for the universality of invariant and equivariant tensorized graph neural networks.
Theorems of stone-weierstrass type for non-compact spaces
L. D. Nel · 1968
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
Earlier work this paper cites.
The rank of connection matrices and the dimension of graph algebras
László Lovász · 2006
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Large networks and graph limits
László Lovász · 2012
Earlier work this paper cites.
Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Cited alongside, same era.
Community detection with graph neural networks
Joan Bruna and X Li · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
Cited alongside, same era.
Equivariance through parameter-sharing
Siamak Ravanbakhsh, Jeff Schneider, and Barnabas Poczos · 2017
Cited alongside, same era.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
Cited alongside, same era.
Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2018
Later among the works it cites.
Universal approximations of invariant maps by neural networks
Dmitry Yarotsky · 2018
Later among the works it cites.
Universal invariant and equivariant graph neural networks
Nicolas Keriven and Gabriel Peyré · 2019
Closest in time.
On the universality of invariant networks
Haggai Maron, Ethan Fetaya, Nimrod Segol, and Yaron Lipman · 2019
Closest in time.
Universal approximations of permutation invariant/equivariant functions by deep neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec · 2017
Cited alongside, same era.
Risi Kondor and Shubhendu Trivedi · 2018
Cited alongside, same era.
Akiyoshi Sannai, Yuuki Takai, and Matthieu Cordonnier · 2019
Closest in time.
Deep graph infomax
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm · 2019
Closest in time.
Making convolutional networks shift-invariant again
Richard Zhang · 2019
Closest in time.