In the paper Junction Tree Variational Autoencoder for Molecular Graph Generation, section 3.2, the authors state that they train a sparse Gaussian process to predict a chemical property, $y(m)$, of a molecule $m$, given the latent representation of the molecule (which is represented by the mean of the variational or hidden distribution).
A Gaussian processes is a collection of infinitely many random variables such that every linear combination of a finite-length subset of those random variables is normally distributed.
What is a sparse Gaussian process? How is it trained? In a supervised way? How does the dataset look like? What do we use a SGP for?