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support equivariant neural networks #221
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I was working with e3nn and nequip this summer and would love to see it in Julia. Something like e3nn.jl sounds exciting. |
have you compared them to the simpler EGNN for your tasks? |
By comparison, do you mean performance or implementation wise? |
Test accuracy. Implementation wise they are very different, e3nn much harder to implement |
No, not yet. We were mainly focused on spherical convolutions. |
Started writing this e3nn.jl library. |
General forms using spherical harmonics
https://www.nature.com/articles/s41467-022-29939-5
https://docs.e3nn.org/en/latest/guide/convolution.html
Simpler equivariant layers
https://proceedings.mlr.press/v139/satorras21a.html
https://github.com/lucidrains/egnn-pytorch
https://docs.dgl.ai/en/0.9.x/generated/dgl.nn.pytorch.conv.EGNNConv.html
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