Implement SE3 Transformers
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- Dominant language
- Python
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Description
Hi, I'm really excited for this library. I think some of the scalability tricks you have promised under the hood look exceptionally nice.
Building off of the GATv2 example, I'd really like to implement SE3 Transformers (https://arxiv.org/pdf/2006.10503.pdf)
I understand you have a roadmap, and this is an alpha, but I haven't found a SE3 implementation in tensorflow I like and this seems the most promising starting point.
I am able to compile and run tests (from my own branch). I can either try and hack something together for myself, or potentially layout a spec with someone from the team to do this.
Contributor guide
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
Read the SE3 Transformers paper and the repository's GATv2 example first. Work with the team to define a specification, including the intended TensorFlow GNN integration and validation criteria; the issue is complete when that agreed implementation and its tests are in place.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100