JuliaGaussianProcesses / JuliaGaussianProcesses/KernelFunctions.jl
matrix-valued kernels for structured multi-output
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enhancement
- Dominant language
- Julia
- Stars
- 275
- Forks
- 41
- PR merge metrics
- No merged PRs in 30d
Description
we would like matrix-valued kernels so that we can learn vector fields! Good starting points would be the divergence-free and curl-free kernels. There exist python and matlab implementations of this. @theogf
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 with the linked divergence-free and curl-free kernel references and compare the linked Python and MATLAB implementations. The issue does not name files, tests, or an existing entry point; define the matrix-valued kernel API and its vector-field behavior before implementation, with coverage for the requested structured kernels as the completion criterion.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100