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Hugh Salimbeni
Hugh Salimbeni
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Cited by
Year
Deep unsupervised clustering with gaussian mixture variational autoencoders
N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ...
arXiv preprint arXiv:1611.02648, 2016
4822016
Doubly stochastic variational inference for deep Gaussian processes
H Salimbeni, M Deisenroth
Advances in neural information processing systems 30, 2017
3092017
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
652018
Gaussian process conditional density estimation
V Dutordoir, H Salimbeni, J Hensman, M Deisenroth
Advances in neural information processing systems 31, 2018
412018
Deep Gaussian processes with importance-weighted variational inference
H Salimbeni, V Dutordoir, J Hensman, M Deisenroth
International Conference on Machine Learning, 5589-5598, 2019
342019
Orthogonally decoupled variational Gaussian processes
H Salimbeni, CA Cheng, B Boots, M Deisenroth
Advances in neural information processing systems 31, 2018
332018
GPflux: A library for deep Gaussian processes
V Dutordoir, H Salimbeni, E Hambro, J McLeod, F Leibfried, A Artemev, ...
arXiv preprint arXiv:2104.05674, 2021
102021
Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv 2017
N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ...
arXiv preprint arXiv:1611.02648, 0
10
A potential biomarker for treatment stratification in psychosis: evaluation of an [18F] FDOPA PET imaging approach
M Veronese, B Santangelo, S Jauhar, E D’Ambrosio, A Demjaha, ...
Neuropsychopharmacology 46 (6), 1122-1132, 2021
82021
Deep unsupervised clustering with Gaussian mixture variational autoencoders. arXiv
N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ...
arXiv preprint arXiv:1611.02648, 2016
82016
Deeply non-stationary Gaussian processes
H Salimbeni, MP Deisenroth
NIPS Workshop on Bayesian Deep Learning, 2017
72017
Stochastic differential equations with variational wishart diffusions
M Jørgensen, M Deisenroth, H Salimbeni
International Conference on Machine Learning, 4974-4983, 2020
42020
Deep Gaussian Processes: Advances in Models and Inference
H Salimbeni
Imperial College London, 2019
12019
Machine learning system
S Eleftheriadis, J Hensman, S John, H Salimbeni
US Patent 10,990,890, 2021
2021
GPflux: ALibraryforDeepGaussianProcesses
V Dutordoir, H Salimbeni, E Hambro, J McLeod, F Leibfried, A Artemev, ...
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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Articles 1–17