Follow
Artem Artemev
Title
Cited by
Cited by
Year
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
702020
Bayesian image classification with deep convolutional Gaussian processes
V Dutordoir, M Wilk, A Artemev, J Hensman
International Conference on Artificial Intelligence and Statistics, 1529-1539, 2020
33*2020
Scalable Thompson sampling using sparse Gaussian process models
S Vakili, H Moss, A Artemev, V Dutordoir, V Picheny
Advances in neural information processing systems 34, 5631-5643, 2021
252021
Doubly Sparse Variational Gaussian Processes
V Adam, S Eleftheriadis, N Durrande, A Artemev, J Hensman
The 23rd International Conference on Artificial Intelligence and Statistics, 2020
182020
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
162021
Tighter bounds on the log marginal likelihood of Gaussian process regression using conjugate gradients
A Artemev, DR Burt, M van der Wilk
International Conference on Machine Learning, 362-372, 2021
82021
Variational Gaussian Process Models without Matrix Inverses
M van der Wilk, ST John, A Artemev, J Hensman
2nd Symposium on Advances in Approximate Bayesian Inference, 2019
62019
Ordinal bayesian optimisation
V Picheny, S Vakili, A Artemev
arXiv preprint arXiv:1912.02493, 2019
52019
Modelling global mRNA dynamics during Drosophila embryogenesis reveals a relationship between mRNA degradation and P-bodies
LF Beadle, JC Love, Y Shapovalova, A Artemev, M Rattray, HL Ashe
bioRxiv, 2022.03. 17.484585, 2022
22022
Improved inverse-free variational bounds for sparse Gaussian processes
M van der Wilk, A Artemev, J Hensman
Fourth Symposium on Advances in Approximate Bayesian Inference, 2022
22022
Barely biased learning for Gaussian process regression
DR Burt, A Artemev, M van der Wilk
arXiv preprint arXiv:2109.09417, 2021
22021
Combined modelling of mRNA decay dynamics and single-molecule imaging in the Drosophila embryo uncovers a role for P-bodies in 5′ to 3′ degradation
L Forbes Beadle, JC Love, Y Shapovalova, A Artemev, M Rattray, ...
PLoS biology 21 (1), e3001956, 2023
12023
Memory safe computations with XLA compiler
A Artemev, T Roeder, M van der Wilk
arXiv preprint arXiv:2206.14148, 2022
12022
Automatic tuning of stochastic gradient descent with bayesian optimisation
V Picheny, V Dutordoir, A Artemev, N Durrande
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021
12021
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
V Picheny, J Berkeley, HB Moss, H Stojic, U Granta, SW Ober, A Artemev, ...
arXiv preprint arXiv:2302.08436, 2023
2023
Efficient computational inference
V Adam, S Eleftheriadis, N Durrande, A Artemev, J Hensman, L Bordeaux
US Patent App. 17/753,723, 2022
2022
Computational implementation of gaussian process models
M VAN DER WILK, S John, A Artemev, J Hensman
US Patent 11,475,279, 2022
2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
A Terenin, DR Burt, A Artemev, S Flaxman, M van der Wilk, ...
arXiv preprint arXiv:2210.07893, 2022
2022
Modelling global mRNA dynamics during Drosophila embryogenesis reveals a relationship between mRNA degradation and P-bodies
JC Love, Y Shapovalova, A Artemev, M Rattray, HL Ashe
2022
Single molecule imaging and modelling of mRNA decay dynamics in the Drosophila embryo
LF Beadle, JC Love, Y Shapovalova, A Artemev, M Rattray, HL Ashe
2022
The system can't perform the operation now. Try again later.
Articles 1–20