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Alexander G. D. G. Matthews
Alexander G. D. G. Matthews
DeepMind
Adresse e-mail validée de google.com
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Scalable Variational Gaussian Process Classification.
J Hensman, A Matthews, Z Ghahramani
The 18th International Conference on Artificial Intelligence and Statistics …, 2015
7772015
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
742*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
594*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
5932019
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
3222021
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
216*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
2032017
Functional Regularisation for Continual Learning with Gaussian Processes
MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh
International Conference on Learning Representations, 2019
1872019
MCMC for variationally sparse Gaussian processes
J Hensman, AG Matthews, M Filippone, Z Ghahramani
Advances in Neural Information Processing Systems, 1648-1656, 2015
1732015
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
85*2010
Variational Bayesian dropout: pitfalls and fixes
J Hron, A Matthews, Z Ghahramani
Proceedings of Machine Learning Research, 2018
832018
Scalable Gaussian process inference using variational methods
AGG Matthews
University of Cambridge, 2017
762017
Annealed flow transport monte carlo
M Arbel, A Matthews, A Doucet
International Conference on Machine Learning, 318-330, 2021
622021
Variational Gaussian Dropout is not Bayesian
J Hron, AGG Matthews, Z Ghahramani
arXiv preprint arXiv:1711.02989, 2017
612017
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
35*2022
Continual Repeated Annealed Flow transport Monte Carlo
A Matthews, M Arbel, DJ Rezende, A Doucet
International Conference on Machine Learning, 15196-15219, 2022
332022
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
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
242023
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
202022
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ...
arXiv preprint arXiv:2305.02402, 2023
182023
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