Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders. - Nat Dilokthanakul; Pedro A. M. Mediano; Marta Garnelo; Matthew C. H. Lee; Hugh Salimbeni; Kai Arulkumaran; Murray Shanahan (CoRR, 2016)

Doubly Stochastic Variational Inference for Deep Gaussian Processes. - Hugh Salimbeni; Marc Peter Deisenroth (NIPS, 2017)

Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. - Hugh Salimbeni; Stefanos Eleftheriadis; James Hensman (AISTATS, 2018)

Gaussian Process Conditional Density Estimation. - Vincent Dutordoir; Hugh Salimbeni; James Hensman; Marc Peter Deisenroth (NeurIPS, 2018)

Orthogonally Decoupled Variational Gaussian Processes. - Hugh Salimbeni; Ching-An Cheng; Byron Boots; Marc Peter Deisenroth (NeurIPS, 2018)

Deep Gaussian Processes with Importance-Weighted Variational Inference. - Hugh Salimbeni; Vincent Dutordoir; James Hensman; Marc Peter Deisenroth (ICML, 2019)

Stochastic Differential Equations with Variational Wishart Diffusions. - Martin Jørgensen; Marc Peter Deisenroth; Hugh Salimbeni (ICML, 2020)

Deep Gaussian processes: advances in models and inference. - Hugh Salimbeni (2020)

GPflux: A Library for Deep Gaussian Processes. - Vincent Dutordoir; Hugh Salimbeni; Eric Hambro; John McLeod; Felix Leibfried; Artem Artemev; Mark van der Wilk; James Hensman; Marc Peter Deisenroth; S. T. John (CoRR, 2021)