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Alexander G. D. G. Matthews
Alexander G. D. G. Matthews
DeepMind
Verified email at google.com
Title
Cited by
Cited by
Year
Scalable Variational Gaussian Process Classification.
J Hensman, A Matthews, Z Ghahramani
The 18th International Conference on Artificial Intelligence and Statistics …, 2015
7112015
GPflow: A Gaussian process library using TensorFlow
AGG Matthews, M van der Wilk, T Nickson, K Fujii, A Boukouvalas, ...
Journal of Machine Learning Research 18 (40), 1-6, 2017
693*2017
Gaussian Process Behaviour in Wide Deep Neural Networks
AGG Matthews, J Hron, M Rowland, RE Turner, Z Ghahramani
International Conference on Learning Representations (ICLR), 2018
543*2018
Ab-Initio Solution of the Many-Electron Schr\" odinger Equation with Deep Neural Networks
D Pfau, JS Spencer, AGG Matthews, WMC Foulkes
arXiv preprint arXiv:1909.02487, 2019
5242019
Pushing the frontiers of density functionals by solving the fractional electron problem
J Kirkpatrick, B McMorrow, DHP Turban, AL Gaunt, JS Spencer, ...
Science 374 (6573), 1385-1389, 2021
2702021
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes
AGG Matthews, J Hensman, RE Turner, Z Ghahramani
The 19th International Conference on Artificial Intelligence and Statistics …, 2016
207*2016
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
J Bradshaw, AGG Matthews, Z Ghahramani
arXiv preprint arXiv:1707.02476, 2017
1982017
Functional Regularisation for Continual Learning with Gaussian Processes
MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh
International Conference on Learning Representations, 2019
1642019
MCMC for variationally sparse Gaussian processes
J Hensman, AG Matthews, M Filippone, Z Ghahramani
Advances in Neural Information Processing Systems, 1648-1656, 2015
1642015
Measurement and simulation of the effect of compaction on the pore structure and saturated hydraulic conductivity of grassland and arable soil
GP Matthews, GM Laudone, AS Gregory, NRA Bird, AG de G Matthews, ...
Water Resources Research 46 (5), 2010
81*2010
Variational Bayesian dropout: pitfalls and fixes
J Hron, A Matthews, Z Ghahramani
Proceedings of Machine Learning Research, 2018
792018
Scalable Gaussian process inference using variational methods
AGG Matthews
University of Cambridge, 2017
692017
Variational Gaussian Dropout is not Bayesian
J Hron, AGG Matthews, Z Ghahramani
arXiv preprint arXiv:1711.02989, 2017
562017
Annealed flow transport monte carlo
M Arbel, A Matthews, A Doucet
International Conference on Machine Learning, 318-330, 2021
522021
Score-based diffusion meets annealed importance sampling
A Doucet, W Grathwohl, AG Matthews, H Strathmann
Advances in Neural Information Processing Systems 35, 21482-21494, 2022
30*2022
Sample-then-optimize posterior sampling for bayesian linear models
AGG Matthews, J Hron, RE Turner, Z Ghahramani
NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2017
302017
Continual Repeated Annealed Flow transport Monte Carlo
A Matthews, M Arbel, DJ Rezende, A Doucet
International Conference on Machine Learning, 15196-15219, 2022
232022
Sampling QCD field configurations with gauge-equivariant flow models
R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ...
arXiv preprint arXiv:2208.03832, 2022
182022
A depth filtration model of straining within the void networks of stainless steel filters
JC Price, GP Matthews, K Quinlan, J Sexton, AGG Matthews
AIChE journal 55 (12), 3134-3144, 2009
172009
Aspects of scaling and scalability for flow-based sampling of lattice QCD
R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ...
The European Physical Journal A 59 (11), 257, 2023
152023
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