tensorflow / tensorflow/probability
Auto-diff for bijector jacobian
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Description
Forgive me if this is a dumb question because I'm new to this. Is implementing auto-diff for computing jacobian in the bijector superclass possible? Doing so should free the users from implementing the individual jacobian calculation in the subclasses once they have implemented the forward/inverse function.
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
No files or tests are named. Start by reading the bijector superclass and comparing subclass forward, inverse, and Jacobian implementations; determine whether automatic differentiation can cover the requested behavior. Done would be a decided, tested approach that removes redundant subclass Jacobian work, if feasible.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- 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