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Found 4 papers, 3 papers with code
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes
Hamiltonian mechanics is one of the cornerstones of natural sciences.
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.
Modelling calibration uncertainty in networks of environmental sensors
Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift.
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.
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