1 citations · 2 across the 5 of their papers we have counts for
5 papers
Thin and Deep Gaussian Processes
Daniel Augusto de Souza, Alexander Nikitin, ST John +6
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the…
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes
Magnus Ross, Markus Heinonen
Hamiltonian mechanics is one of the cornerstones of natural sciences. Recently there has been significant interest in learning Hamiltonian systems in a free-form way directly from…
Shallow and Deep Nonparametric Convolutions for Gaussian Processes
Thomas M. McDonald, Magnus Ross, Michael T. Smith +1
A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper covariance function. The moving average, or process convolutions, construction of GPs…
Modelling calibration uncertainty in networks of environmental sensors
Michael Thomas Smith, Magnus Ross, Joel Ssematimba +4
Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complic…
Learning Nonparametric Volterra Kernels with Gaussian Processes
Magnus Ross, Michael T. Smith, Mauricio A. Álvarez
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 proces…