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Found 4 papers, 3 papers with code
Neural Non-Stationary Spectral Kernel
Spectral mixture kernels have been proposed as general-purpose, flexible kernels for learning and discovering more complicated patterns in the data.
Non-Stationary Spectral Kernels
We propose non-stationary spectral kernels for Gaussian process regression.
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
We introduce a novel kernel that models input-dependent couplings across multiple latent processes.
Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers
We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA).
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