ST John
ST John
Secondmind (previously known as PROWLER.io)
Adresse e-mail validée de prowler.io
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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
602017
Large-scale Cox process inference using variational Fourier features
ST John, J Hensman
International Conference on Machine Learning, 2362-2370, 2018
212018
Learning invariances using the marginal likelihood
M van der Wilk, M Bauer, ST John, J Hensman
arXiv preprint arXiv:1808.05563, 2018
202018
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
152011
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
122020
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
92019
Scalable GAM using sparse variational Gaussian processes
V Adam, N Durrande, ST John
arXiv preprint arXiv:1812.11106, 2018
22018
A Tutorial on Sparse Gaussian Processes and Variational Inference
F Leibfried, V Dutordoir, ST John, N Durrande
arXiv preprint arXiv:2012.13962, 2020
12020
Amortized variance reduction for doubly stochastic objective
A Boustati, S Vakili, J Hensman, ST John
Conference on Uncertainty in Artificial Intelligence, 61-70, 2020
12020
Learning invariances using the marginal likelihood
M Wilk, M Bauer, ST John, J Hensman
Proceedings of the 32nd International Conference on Neural Information …, 2018
12018
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
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