JuliaGaussianProcesses / JuliaGaussianProcesses/AbstractGPs.jl
Deep kernel learning example: add mini-batching
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good first issue
- Dominant language
- Julia
- Stars
- 287
- Forks
- 27
- PR merge metrics
- No merged PRs in 30d
Description
This should prevent the overfitting issue, as discussed by Ober et al.
Contributor guide
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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 locating the deep kernel learning example and reading how its training loop currently handles data. Determine the mini-batching approach needed for that example, then verify that the example runs with mini-batches and addresses the overfitting issue described in the report.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100