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JuliaGaussianProcesses / AbstractGPs.jl

Abstract types and methods for Gaussian Processes.
https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev
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Deep kernel learning example: performance #298

Open st-- opened 2 years ago

st-- commented 2 years ago

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?

willtebbutt commented 2 years ago

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?

By this, do you mean the fact that we have to both compute the log marginal likelihood and the posterior each time that we want to plot, meaning that we're probably doing roughly double the amount of work that we need to?