Alexander G. de G. Matthews

According to our database, Alexander G. de G. Matthews authored at least 14 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Normalizing flows for lattice gauge theory in arbitrary space-time dimension.

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2023

2022

Aspects of scaling and scalability for flow-based sampling of lattice QCD.

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2022

Score-Based Diffusion meets Annealed Importance Sampling.

Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Continual Repeated Annealed Flow Transport Monte Carlo.

Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet

Proceedings of the International Conference on Machine Learning, 2022

2021

Annealed Flow Transport Monte Carlo.

Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Functional Regularisation for Continual Learning with Gaussian Processes.

Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks.

David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. Matthew C. Foulkes

CoRR, 2019

Functional Regularisation for Continual Learning using Gaussian Processes.

Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh

CoRR, 2019

2018

Variational Bayesian dropout: pitfalls and fixes.

Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani

Proceedings of the 35th International Conference on Machine Learning, 2018

Gaussian Process Behaviour in Wide Deep Neural Networks.

Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

GPflow: A Gaussian Process Library using TensorFlow.

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman

J. Mach. Learn. Res., 2017

2016

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes.

Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

MCMC for Variationally Sparse Gaussian Processes.

James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Scalable Variational Gaussian Process Classification.

James Hensman, Alexander G. de G. Matthews, Zoubin Ghahramani

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015