Alexander G. D. G. Matthews
Alexander G. D. G. Matthews
Verified email at
Cited by
Cited by
Scalable Variational Gaussian Process Classification.
J Hensman, A Matthews, Z Ghahramani
The 18th International Conference on Artificial Intelligence and Statistics …, 2015
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
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
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
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
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
MCMC for variationally sparse Gaussian processes
J Hensman, AG Matthews, M Filippone, Z Ghahramani
Advances in Neural Information Processing Systems, 1648-1656, 2015
Functional Regularisation for Continual Learning with Gaussian Processes
MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh
International Conference on Learning Representations, 2019
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
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
Variational Bayesian dropout: pitfalls and fixes
J Hron, A Matthews, Z Ghahramani
Proceedings of Machine Learning Research, 2018
Scalable Gaussian process inference using variational methods
AGG Matthews
University of Cambridge, 2017
Variational Gaussian Dropout is not Bayesian
J Hron, AGG Matthews, Z Ghahramani
arXiv preprint arXiv:1711.02989, 2017
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
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
Annealed flow transport monte carlo
M Arbel, A Matthews, A Doucet
International Conference on Machine Learning, 318-330, 2021
Scattering theory for quantum Hall anyons in a saddle point potential
A Matthews, NR Cooper
Physical Review B 80 (16), 165309, 2009
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
Continual Repeated Annealed Flow transport Monte Carlo
A Matthews, M Arbel, DJ Rezende, A Doucet
International Conference on Machine Learning, 15196-15219, 2022
Estimation of structural element sizes in sand and compacted blocks of ground calcium carbonate using a void network model
GM Laudone, GP Matthews, PAC Gane, AG Matthews, CJ Ridgway, ...
Transport in porous media 66 (3), 403-419, 2007
The system can't perform the operation now. Try again later.
Articles 1–20