Add model with inner optimization loop to model repo
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- Dominant language
- Python
- Stars
- 1k
- Forks
- 346
- PR merge metrics
- No merged PRs in 30d
Description
An inner optimization loop is when the functional autograd API is used in the forward pass and then the user differentiates through it. Uses include MAML, Energy Based Models, and (shameless self plug) set generation.
Would be nice as it requires autograd in the forward pass, and requires higher order gradients in the backwards pass. Definitely more "researchy" code.
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
The issue does not name a file, test, or entry point. Start by reviewing the model repository's conventions and the linked MAML, energy-based model, and set-generation examples, then determine how an inner optimization loop should fit the benchmark collection. Done should mean a model using functional autograd in its forward pass and higher-order gradients in backward is added to the repository.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100