ST John
ST John
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Many-body coarse-grained interactions using Gaussian approximation potentials
ST John, G Csányi
The Journal of Physical Chemistry B 121 (48), 10934-10949, 2017
Large-scale Cox process inference using variational Fourier features
ST John, J Hensman
arXiv preprint arXiv:1804.01016, 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
Gaussian process modulated cox processes under linear inequality constraints
AF López-Lopera, ST John, N Durrande
arXiv preprint arXiv:1902.10974, 2019
Learning invariances using the marginal likelihood
M van der Wilk, M Bauer, ST John, J Hensman
Advances in Neural Information Processing Systems, 9938-9948, 2018
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
Amortized variance reduction for doubly stochastic objectives
A Boustati, S Vakili, J Hensman, ST John
arXiv preprint arXiv:2003.04125, 2020
Scalable GAM using sparse variational Gaussian processes
V Adam, N Durrande, ST John
arXiv preprint arXiv:1812.11106, 2018
Machine learning system
S Eleftheriadis, J Hensman, S John, H Salimbeni
US Patent App. 16/824,025, 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
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
Theoretical Studies of a Method to Measure the Superfluid Fraction of an Ultracold Atomic Gas
ST John
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