Alexander G. de G. Matthews
Alexander G. de G. Matthews
DeepMind
Verified email at google.com
Title
Cited by
Cited by
Year
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
239*2017
Scalable Variational Gaussian Process Classification.
J Hensman, A Matthews, Z Ghahramani
The 18th International Conference on Artificial Intelligence and Statistics …, 2015
2302015
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
135*2018
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
92*2016
MCMC for variationally sparse Gaussian processes
J Hensman, AG Matthews, M Filippone, Z Ghahramani
Advances in Neural Information Processing Systems, 1648-1656, 2015
752015
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
562010
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
482017
Scalable Gaussian process inference using variational methods
AGG Matthews
University of Cambridge, 2017
302017
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
28*2019
Variational Bayesian dropout: pitfalls and fixes
J Hron, A Matthews, Z Ghahramani
Proceedings of Machine Learning Research, 2018
27*2018
Variational Gaussian Dropout is not Bayesian
J Hron, AGG Matthews, Z Ghahramani
arXiv preprint arXiv:1711.02989, 2017
172017
Functional Regularisation for Continual Learning with Gaussian Processes
MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh
International Conference on Learning Representations, 2019
15*2019
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
142009
Sample-then-optimize posterior sampling for Bayesian linear models
AGG Matthews, J Hron, RE Turner, Z Ghahramani
NIPS Workshop on Advances in Approximate Bayesian Inference, 2017
42017
Scattering theory for quantum Hall anyons in a saddle point potential
A Matthews, NR Cooper
Physical Review B 80 (16), 165309, 2009
42009
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
32007
Classification using log Gaussian Cox processes
AG Matthews, Z Ghahramani
arXiv preprint arXiv:1405.4141, 2014
12014
The full natural gradient of a sparse variational Gaussian process
AGG Matthews
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Articles 1–18