Alexander G. de G. Matthews

19 papers A* 5A 2Journal 10Unranked 2

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