research
∙
07/11/2023
Geometric Neural Diffusion Processes
Denoising diffusion models have proven to be a flexible and effective pa...
share
research
∙
04/27/2023
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Despite their many desirable properties, Gaussian processes (GPs) are of...
research
∙
02/06/2023
Memory-Based Meta-Learning on Non-Stationary Distributions
Memory-based meta-learning is a technique for approximating Bayes-optima...
research
∙
06/08/2022
Neural Diffusion Processes
Gaussian processes provide an elegant framework for specifying prior and...
research
∙
05/10/2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Deep Gaussian processes (DGPs) have struggled for relevance in applicati...
research
∙
04/12/2021
GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a ...
research
∙
12/27/2020
A Tutorial on Sparse Gaussian Processes and Variational Inference
Gaussian processes (GPs) provide a framework for Bayesian inference that...
research
∙
06/30/2020
Sparse Gaussian Processes with Spherical Harmonic Features
We introduce a new class of inter-domain variational Gaussian processes ...
research
∙
06/25/2020
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Many machine learning models require a training procedure based on runni...
research
∙
03/02/2020
A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in large-scale probl...
research
∙
05/14/2019
Deep Gaussian Processes with Importance-Weighted Variational Inference
Deep Gaussian processes (DGPs) can model complex marginal densities as w...
research
∙
02/15/2019
Translation Insensitivity for Deep Convolutional Gaussian Processes
Deep learning has been at the foundation of large improvements in image ...
research
∙
10/30/2018