Andrew Gordon Wilson: Translation equivariance on images gives CNNs key generalization abilities. Our new paper "Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data": https://arxiv.org/abs/2002.12880. With @m_finzi, @sam_d_stanton, @Pavel_Izmailov. 1/6 https://t.co/E3LbVcDoZl
9 replies, 404 likes
Simone Scardapane: *Generalizing CNNs for Equivariance to Lie Groups on Arbitrary Continuous Data*
[by @m_finzi @sam_d_stanton @Pavel_Izmailov @andrewgwils]
Fantastic paper on building convolutional nets that are equivariant to a wide range of transformations.
0 replies, 60 likes
Erik Bekkers: Very interesting work on Lie group equivariant NNs! Some aspects I really like: a way to handle inputs on some space X that is not a homogenous space of G (not done before! G could be smaller than X) and parameterizing kernels with MLPs. Here I give my analysis of the paper. 1/7
1 replies, 43 likes
HotComputerScience: Most popular computer science paper of the day:
"Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data"
0 replies, 43 likes
Sam Stanton: How do you use the same neural net architecture to learn equivariant models for molecules, physical systems, and images? Find out in our new paper!
0 replies, 23 likes
Maurice Weiler: This is a neat variation of group equivariant convolutions which seems easily applicable to a range of applications beyond image processing. The method is independent from the symmetry group and sampling grids.
1 replies, 16 likes
Kyle Cranmer: Check it out 👇
0 replies, 11 likes
andrea panizza: Year of the Lie Group equivariance! @erikjbekkers @TacoCohen @maurice_weiler @_gabrielecesa_ your citations are on the rise 😀
0 replies, 10 likes
Daisuke Okanohara: We can make convolution layers equivariant for any transformations from Lie group as long as it supports group exp/log maps. Data can be located in arbitrary continuous positions/states (e.g., point cloud, molecules, dynamical system). https://arxiv.org/abs/2002.12880
0 replies, 7 likes
Statistics Papers: Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. http://arxiv.org/abs/2002.12880
0 replies, 7 likes
Kyle Cranmer: @jonkhler @_onionesque @erikjbekkers @MilesCranmer @wellingmax @DaniloJRezende There is also this work Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data from
Marc Finzi, Samuel Stanton, Pavel Izmailov,@andrewgwils
0 replies, 2 likes
Miles Cranmer: 4/10 @erikjbekkers - https://arxiv.org/abs/1909.12057 (images/grids) & @m_finzi - https://arxiv.org/abs/2002.12880 (alt. technique + extends to point clouds + Hamiltonian GNs) - Enforce symmetry in CNNs over a variety of Lie groups (2D rotational symmetry ~ "SO(2) Lie group") https://t.co/zcLM1FdB4a
1 replies, 2 likes
Found on Mar 02 2020 at https://arxiv.org/pdf/2002.12880.pdf