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Found 3 papers, 3 papers with code
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data.
Deep learning with differential Gaussian process flows
We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression function.
Variational zero-inflated Gaussian processes with sparse kernels
We propose a novel model family of zero-inflated Gaussian processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels through learning a latent probit Gaussian process that can zero out kernel rows and columns whenever the signal is absent.
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