Iain Murray

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David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeVerified email at mrao.cam.ac.uk
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
George PapamakariosDeepMindVerified email at google.com
Hugo LarochelleGoogle Brain & MilaVerified email at google.com
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Benigno UríaGoogle DeepMindVerified email at google.com
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Steve RenalsProfessor of Speech Technology, University of EdinburghVerified email at ed.ac.uk
Ben KrauseResearch Scientist, SalesforceVerified email at salesforce.com
Conor DurkanDeepMindVerified email at deepmind.com
Theo PavlakouPhD student, The University of EdinburghVerified email at ed.ac.uk
Artur BekasovUniversity of EdinburghVerified email at ed.ac.uk
Mathieu GermainResearcher @ MilaVerified email at usherbrooke.ca
David MimnoAssociate Professor, Cornell UniversityVerified email at cornell.edu
Hanna WallachPartner Research Manager, Microsoft ResearchVerified email at dirichlet.net
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Asa Cooper SticklandPhD student, University of EdinburghVerified email at ed.ac.uk
Liang LuStaff Speech Scientist, StartupVerified email at ttic.edu
Emmanuel KahembweDoctoral Candidate, The University of Edinburgh, Heriot-Watt University & The Edinburgh Centre forVerified email at ed.ac.uk
George E. DahlGoogle Inc.Verified email at google.com

Iain Murray

School of Informatics, University of Edinburgh

Verified email at ed.ac.uk - Homepage

Machine Learning Bayesian Inference Hierarchical probabilistic models Markov chain Monte Carlo Density Estimation


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009	956	2009
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray arXiv preprint arXiv:1705.07057, 2017	566	2017
The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the fourteenth international conference on artificial …, 2011	525	2011
On the quantitative analysis of deep belief networks R Salakhutdinov, I Murray Proceedings of the 25th international conference on Machine learning, 872-879, 2008	503	2008
MADE: Masked Autoencoder for Distribution Estimation M Germain, K Gregor, I Murray, H Larochelle Proceedings of the 32nd International Conference on Machine Learning, JMLR W …, 2015	467	2015
MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006	444*	2006
Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010	418	2010
Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams arXiv preprint arXiv:1006.0868, 2010	230	2010
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities RP Adams, I Murray, DJC MacKay Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	229	2009
Neural autoregressive distribution estimation B Uria, MA Côté, K Gregor, I Murray, H Larochelle The Journal of Machine Learning Research 17 (1), 7184-7220, 2016	227	2016
RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle arXiv preprint arXiv:1306.0186, 2013	183	2013
Neural Spline Flows C Durkan, A Bekasov, I Murray, G Papamakarios arXiv preprint arXiv:1906.04032, 2019	156	2019
Fast ε-free inference of simulation models with bayesian conditional density estimation G Papamakarios, I Murray Advances in neural information processing systems, 1028-1036, 2016	155	2016
A Framework for Evaluating Approximation Methods for Gaussian Process Regression K Chalupka, CKI Williams, I Murray Journal of Machine Learning Research 14, 333-350, 2013	152	2013
Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016	144	2016
A deep and tractable density estimator B Uria, I Murray, H Larochelle Proceedings of The 31st International Conference on Machine Learning, JMLR W …, 2014	136	2014
Incorporating side information into probabilistic matrix factorization using Gaussian processes RP Adams, GE Dahl, I Murray Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty …, 2010	118*	2010
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows G Papamakarios, D Sterratt, I Murray The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	111	2019
Advances in Markov chain Monte Carlo methods I Murray University College London, 2007	111	2007
Bayesian learning in undirected graphical models: approximate MCMC algorithms I Murray, Z Ghahramani arXiv preprint arXiv:1207.4134, 2012	110	2012

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