Implement `RelaxedOneHotCategoricalStraightThrough`
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- Dominant language
- Python
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Description
Following #548 discussion, and while we wait for discrete latent variables, it would be nice to have a Gumbel-Softmax categorical approximation as featured in Pyro. Didn't realize this was the name given to Gumbel-Softmax in Pyro, but hopefully replication might be straight-forward?
numpyro (i.e. Jax) seems uniquely suited for problems involving large discrete structures (e.g. networks), so an ability to recover latent discrete variables (or their approximations) would be fantastic!
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
Start by reading the #548 discussion and the corresponding Pyro implementation of RelaxedOneHotCategoricalStraightThrough. Determine how the distribution should fit NumPyro's existing distribution APIs; done means the named approximation is implemented and its behavior matches the intended Pyro semantics.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100