Many-body coarse-grained interactions using Gaussian approximation potentials ST John, G Csányi The Journal of Physical Chemistry B 121 (48), 10934-10949, 2017 | 60 | 2017 |
Large-scale Cox process inference using variational Fourier features ST John, J Hensman International Conference on Machine Learning, 2362-2370, 2018 | 21 | 2018 |
Learning invariances using the marginal likelihood M van der Wilk, M Bauer, ST John, J Hensman arXiv preprint arXiv:1808.05563, 2018 | 20 | 2018 |
Spectroscopic method to measure the superfluid fraction of an ultracold atomic gas ST John, Z Hadzibabic, NR Cooper Physical Review A 83 (2), 023610, 2011 | 15 | 2011 |
A framework for interdomain and multioutput Gaussian processes M van der Wilk, V Dutordoir, ST John, A Artemev, V Adam, J Hensman arXiv preprint arXiv:2003.01115, 2020 | 12 | 2020 |
Gaussian process modulated cox processes under linear inequality constraints AF López-Lopera, ST John, N Durrande The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 9 | 2019 |
Scalable GAM using sparse variational Gaussian processes V Adam, N Durrande, ST John arXiv preprint arXiv:1812.11106, 2018 | 2 | 2018 |
A Tutorial on Sparse Gaussian Processes and Variational Inference F Leibfried, V Dutordoir, ST John, N Durrande arXiv preprint arXiv:2012.13962, 2020 | 1 | 2020 |
Amortized variance reduction for doubly stochastic objective A Boustati, S Vakili, J Hensman, ST John Conference on Uncertainty in Artificial Intelligence, 61-70, 2020 | 1 | 2020 |
Learning invariances using the marginal likelihood M Wilk, M Bauer, ST John, J Hensman Proceedings of the 32nd International Conference on Neural Information …, 2018 | 1 | 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 | | 2021 |
Computational inference system A Boustati, S John, S Vakili, J Hensman US Patent App. 16/984,824, 2021 | | 2021 |
Supplementary material for ‘Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments’. N BinTayyash, S Georgaka, ST John, A Boukouvalas, S Ahmed, ... | | 2021 |
Machine learning system A Tukiainen, D Kim, T Nicholson, M Tomczak, JEMDEC FLORES, ... US Patent App. 16/753,580, 2020 | | 2020 |
Machine learning system S Eleftheriadis, J Hensman, S John, H Salimbeni US Patent App. 16/824,025, 2020 | | 2020 |
Variational Gaussian Process Models without Matrix Inverses M van der Wilk, ST John, A Artemev, J Hensman Symposium on Advances in Approximate Bayesian Inference, 1-9, 2020 | | 2020 |
Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments N BinTayyash, S Georgaka, ST John, S Ahmed, A Boukouvalas, ... bioRxiv, 2020 | | 2020 |
Theoretical Studies of a Method to Measure the Superfluid Fraction of an Ultracold Atomic Gas ST John | | 2012 |
On the question of the reliability of silent gene frequencies derived from maximum-likelihood estimates K Hummel, S John Advances in Forensic Haemogenetics, 631-634, 1988 | | 1988 |
A Framework for Interdomain and Multioutput Gaussian Processes Download PDF Open Website M van der Wilk, V Dutordoir, ST John, A Artemev, V Adam, J Hensman | | |