Hugh Salimbeni
Hugh Salimbeni
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Deep unsupervised clustering with gaussian mixture variational autoencoders
N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ...
arXiv preprint arXiv:1611.02648, 2016
Doubly stochastic variational inference for deep Gaussian processes
H Salimbeni, M Deisenroth
Advances in neural information processing systems, 4588-4599, 2017
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
H Salimbeni, S Eleftheriadis, J Hensman
International Conference on Artificial Intelligence and Statistics, 2018
Gaussian process conditional density estimation
V Dutordoir, H Salimbeni, J Hensman, M Deisenroth
Advances in neural information processing systems, 2385-2395, 2018
Orthogonally decoupled variational gaussian processes
H Salimbeni, CA Cheng, B Boots, M Deisenroth
Advances in neural information processing systems, 8711-8720, 2018
Deep gaussian processes with importance-weighted variational inference
H Salimbeni, V Dutordoir, J Hensman, MP Deisenroth
arXiv preprint arXiv:1905.05435, 2019
Deeply non-stationary Gaussian processes
H Salimbeni, MP Deisenroth
Proc. NIPS Workshop Bayesian Deep Learn., 2017
A potential biomarker for treatment stratification in psychosis: evaluation of an [18 F] FDOPA PET imaging approach
M Veronese, B Santangelo, S Jauhar, E D’Ambrosio, A Demjaha, ...
Neuropsychopharmacology, 1-11, 2020
Stochastic Differential Equations with Variational Wishart Diffusions
M Jørgensen, M Deisenroth, H Salimbeni
International Conference on Machine Learning, 4974-4983, 2020
Machine learning system
S Eleftheriadis, J Hensman, S John, H Salimbeni
US Patent App. 16/824,025, 2020
Deep Gaussian Processes: Advances in Models and Inference
H Salimbeni
Imperial College London, 2019
Doubly Stochastic Inference for Deep Gaussian Processes
H Salimbeni
Patch kernels for Gaussian processes in high-dimensional imaging problems
MCH Lee, H Salimbeni, MP Deisenroth, B Glocker
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