308 citations · 528 across the 45 of their papers we have counts for
4 papers · 1 filter
Non-Factorised Variational Inference in Dynamical Systems
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman +1
We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process. The dominant approach so f…
Closed-form Inference and Prediction in Gaussian Process State-Space Models
Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen
We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling.…
Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk +1
We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty. It extends neural network libraries with drop-in replacements for common la…
Learning Invariances using the Marginal Likelihood
Mark van der Wilk, Matthias Bauer, ST John +1
Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the…