| Deep unsupervised clustering with gaussian mixture variational autoencoders N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ... arXiv preprint arXiv:1611.02648, 2016 | 700 | 2016 |
| Doubly stochastic variational inference for deep Gaussian processes H Salimbeni, M Deisenroth Advances in neural information processing systems 30, 2017 | 488 | 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 | 102 | 2018 |
| Gaussian process conditional density estimation V Dutordoir, H Salimbeni, J Hensman, M Deisenroth Advances in neural information processing systems 31, 2018 | 63 | 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 | 58 | 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 | 52 | 2021 |
| Orthogonally decoupled variational Gaussian processes H Salimbeni, CA Cheng, B Boots, M Deisenroth Advances in neural information processing systems 31, 2018 | 50 | 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 | 29 | 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 | 4 | 2020 |
| Machine learning system S Eleftheriadis, J Hensman, S John, H Salimbeni US Patent 10,990,890, 2021 | 3 | 2021 |
| [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 | | |
| Patch kernels for Gaussian processes in high-dimensional imaging problems MCH Lee, H Salimbeni, MP Deisenroth, B Glocker | | |
Marc DeisenrothUniversity College London and The Alan Turing InstituteBestätigte E-Mail-Adresse bei ucl.ac.uk
