Deep unsupervised clustering with gaussian mixture variational autoencoders N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ... arXiv preprint arXiv:1611.02648, 2016 | 728 | 2016 |
Doubly stochastic variational inference for deep Gaussian processes H Salimbeni, M Deisenroth Advances in neural information processing systems 30, 2017 | 501 | 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 | 109 | 2018 |
Gaussian process conditional density estimation V Dutordoir, H Salimbeni, J Hensman, M Deisenroth Advances in neural information processing systems 31, 2018 | 70 | 2018 |
Deep Gaussian processes with importance-weighted variational inference H Salimbeni, V Dutordoir, J Hensman, M Deisenroth International Conference on Machine Learning, 5589-5598, 2019 | 59 | 2019 |
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 | 55 | 2021 |
Orthogonally decoupled variational Gaussian processes H Salimbeni, CA Cheng, B Boots, M Deisenroth Advances in neural information processing systems 31, 2018 | 53 | 2018 |
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 | 31 | 2021 |
Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv 2016 N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ... arXiv preprint arXiv:1611.02648, 0 | 16 | |
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 | 14 | 2016 |
Stochastic differential equations with variational wishart diffusions M Jørgensen, M Deisenroth, H Salimbeni International Conference on Machine Learning, 4974-4983, 2020 | 10 | 2020 |
Deeply non-stationary Gaussian processes H Salimbeni, MP Deisenroth NIPS Workshop on Bayesian Deep Learning, 2017 | 9 | 2017 |
Deep unsupervised clustering with gaussian mixture variational autoencoders D Nat, AMM Pedro, G Marta, CHL Matthew, H Salimbeni, A Kai arXiv preprint arXiv:1611.02648, 2016 | 9 | 2016 |
Deep Gaussian processes: advances in models and inference H Salimbeni Imperial College London, 2020 | 5 | 2020 |
Machine learning system S Eleftheriadis, J Hensman, S John, H Salimbeni US Patent 10,990,890, 2021 | 3 | 2021 |
Patch kernels for Gaussian processes in high-dimensional imaging problems MCH Lee, H Salimbeni, MP Deisenroth, B Glocker NIPS Workshop on Practical Bayesian Nonparametrics, 2016 | 1 | 2016 |
[18F] FDOPA PET imaging for prediction of treatment response in psychosis G Nordio, R Easmin, B Santangelo, S Jauhar, E d'Ambrosio, A Demjaha, ... JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM 41 (1_ SUPPL), 51-52, 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 | | |