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Michael Thomas Smith

2 accepted papers

2022

Adjoint-aided inference of Gaussian process driven differential equations

NeurIPS 2022accept

Linear systems occur throughout engineering and the sciences, most notably as differential equations. In many cases the forcing function for the system is unknown, and interest lies in using noisy observations of the system to infer the forcing, as well as other unknown parameters. In differential e…

Cited by 7SourcePDFScholar
2021

Learning Nonparametric Volterra Kernels with Gaussian Processes

NeurIPS 2021poster

This paper introduces a method for the nonparametric Bayesian learning of nonlinear operators, through the use of the Volterra series with kernels represented using Gaussian processes (GPs), which we term the nonparametric Volterra kernels model (NVKM). When the input function to the operator is uno…