JuliaGraphs / JuliaGraphs/GraphNeuralNetworks.jl
support equivariant neural networks
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 308
- Forks
- 74
- Avg merge
- 3d 6h
- Merged PRs (30d)
- 2
Description
-
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
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked Nature paper and e3nn convolution guide to define the requested general spherical-harmonics forms. Compare them with the simpler equivariant-layer references already checked, then inspect the repository’s existing graph neural network layers to determine the integration scope. Done means general equivariant neural network support is implemented and documented with appropriate validation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100