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Found 3 papers, 1 papers with code
Input Dependent Sparse Gaussian Processes
For this, we use a neural network that receives the observed data as an input and outputs the inducing points locations and the parameters of $q$.
Multi-class Gaussian Process Classification with Noisy Inputs
The results obtained show that, although the classification error is similar across methods, the predictive distribution of the proposed methods is better, in terms of the test log-likelihood, than the predictive distribution of a classifier based on GPs that ignores input noise.
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation
Furthermore, extra assumptions in the approximate inference process make the memory cost independent of $N$.
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