JuliaGaussianProcesses / JuliaGaussianProcesses/AbstractGPs.jl
Deep kernel learning example: performance
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- Dominant language
- Julia
- Stars
- 287
- Forks
- 27
- PR merge metrics
- No merged PRs in 30d
Description
It's currently a rather slow notebook.
For example, it seems rather inefficient that we have to compute posterior(fx, y_train) all over whenever we want to plot... isn't there some way to get it once together with the gradients?
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 notebook and profiling the repeated posterior(fx, y_train) calculations used while plotting. Read how the notebook currently obtains posterior values and gradients, then determine whether they can be computed together without changing the example's results. Done means the notebook avoids unnecessary repeated work and its plotting behavior remains correct.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100