498 citations · 640 across the 8 of their papers we have counts for
8 papers
GPflux: A Library for Deep Gaussian Processes
Vincent Dutordoir, Hugh Salimbeni, Eric Hambro +7
We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing DGPs is a challenging endeavour due to the v…
Stochastic Differential Equations with Variational Wishart Diffusions
Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni
We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time dynamical modelling. The work has high emphasi…
Deep Gaussian Processes with Importance-Weighted Variational Inference
Hugh Salimbeni, Vincent Dutordoir, James Hensman +1
Deep Gaussian processes (DGPs) can model complex marginal densities as well as complex mappings. Non-Gaussian marginals are essential for modelling real-world data, and can be gene…
Gaussian Process Conditional Density Estimation
Vincent Dutordoir, Hugh Salimbeni, Marc Deisenroth +1
Conditional Density Estimation (CDE) models deal with estimating conditional distributions. The conditions imposed on the distribution are the inputs of the model. CDE is a challen…
Orthogonally Decoupled Variational Gaussian Processes
Hugh Salimbeni, Ching-An Cheng, Byron Boots +1
Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions. Despite appealing theory, its superlinear computational and memory complexities h…
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman
The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored. We examine how natural gradients…
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Hugh Salimbeni, Marc Deisenroth
Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussia…
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo +4
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep gene…