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20232026
most citedScalable Multi-Output Gaussian Processes with Stochastic Variational Inference

2 citations · 2 across the 8 of their papers we have counts for

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stat.ML2023

Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning

Thomas Baldwin-McDonald, Mauricio A. Álvarez

Modelling the behaviour of highly nonlinear dynamical systems with robust uncertainty quantification is a challenging task which typically requires approaches specifically designed…

stat.ML2023

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…