Alexander G. de G. Matthews

Publication Activity (10 Years)

Years Active: 2015-2023
Publications (10 Years): 19

Top Topics

Gaussian Process

Top Venues

Publications

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
Normalizing flows for lattice gauge theory in arbitrary space-time dimension. CoRR (2023)
Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann
Score-Based Diffusion meets Annealed Importance Sampling. CoRR (2022)
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
Aspects of scaling and scalability for flow-based sampling of lattice QCD. CoRR (2022)
Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann
Score-Based Diffusion meets Annealed Importance Sampling. NeurIPS (2022)
Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet
Continual Repeated Annealed Flow Transport Monte Carlo. ICML (2022)
Alexander G. de G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet
Continual Repeated Annealed Flow Transport Monte Carlo. CoRR (2022)
Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet
Annealed Flow Transport Monte Carlo. ICML (2021)
Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet
Annealed Flow Transport Monte Carlo. CoRR (2021)
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh
Functional Regularisation for Continual Learning with Gaussian Processes. ICLR (2020)
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh
Functional Regularisation for Continual Learning using Gaussian Processes. CoRR (2019)
David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. Matthew C. Foulkes
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks. CoRR (2019)
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani
Variational Bayesian dropout: pitfalls and fixes. CoRR (2018)
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani
Variational Bayesian dropout: pitfalls and fixes. ICML (2018)
Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani
Gaussian Process Behaviour in Wide Deep Neural Networks. ICLR (Poster) (2018)
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
Gaussian Process Behaviour in Wide Deep Neural Networks. CoRR (2018)
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman
GPflow: A Gaussian Process Library using TensorFlow. J. Mach. Learn. Res. 18 (2017)
Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. AISTATS (2016)
James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani
MCMC for Variationally Sparse Gaussian Processes. NIPS (2015)
James Hensman, Alexander G. de G. Matthews, Zoubin Ghahramani
Scalable Variational Gaussian Process Classification. AISTATS (2015)