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Found 4 papers, 2 papers with code
Shallow and Deep Nonparametric Convolutions for Gaussian Processes
A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper covariance function.
Learning Nonparametric Volterra Kernels with Gaussian Processes
When the input function to the operator is unobserved and has a GP prior, the NVKM constitutes a powerful method for both single and multiple output regression, and can be viewed as a nonlinear and nonparametric latent force model.
Machine Learning for a Low-cost Air Pollution Network
Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used.
Killing four birds with one Gaussian process: the relation between different test-time attacks
For example, we are able to secure GPC against empirical membership inference by proper configuration.
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