Iain Murray - Google Scholar Citations

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	All	Since 2014
Citations	4292	3463
h-index	26	26
i10-index	35	32

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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
Hugo LarochelleGoogle BrainVerified email at usherbrooke.ca
Ruslan SalakhutdinovAssociate Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
George PapamakariosDeepMindVerified email at google.com
Zoubin GhahramaniProfessor, University of Cambridge, and Chief Scientist, UberVerified email at eng.cam.ac.uk
Benigno UríaGoogle DeepMindVerified email at google.com
Steve RenalsProfessor of Speech Technology, University of EdinburghVerified email at ed.ac.uk
Ben KrausePhD Student, University of EdinburghVerified email at ed.ac.uk
David MimnoAssistant Professor, Cornell UniversityVerified email at cornell.edu
Hanna WallachSenior Principal Researcher, Microsoft ResearchVerified email at dirichlet.net
Mathieu GermainResearcher @ MilaVerified email at usherbrooke.ca
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
peter lathamProfessor, Gatsby Computational Neuroscience Unit, UCLVerified email at gatsby.ucl.ac.uk
Krzysztof ChalupkaCalifornia Institute of TechnologyVerified email at caltech.edu
Robert NishiharaPhD student, UC BerkeleyVerified email at eecs.berkeley.edu
George E. DahlGoogle BrainVerified email at cs.toronto.edu
David W HoggNew York University & MPIA & Center for Computational Astrophysics, Flatiron InstituteVerified email at nyu.edu
Jakob MackeComputational Neuroengineering, TU MünchenVerified email at tum.de
Korin RichmondCentre for Speech Technology Research, School of Informatics, University of EdinburghVerified email at cstr.ed.ac.uk

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	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	704	2009
On the quantitative analysis of deep belief networks R Salakhutdinov, I Murray Proceedings of the 25th international conference on Machine learning, 872-879, 2008	394	2008
MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006	355*	2006
The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the Fourteenth International Conference on Artificial …, 2011	340	2011
Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010	320	2010
Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams Advances in neural information processing systems, 1732-1740, 2010	193	2010
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	182	2015
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	169	2009
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in Neural Information Processing Systems, 2338-2347, 2017	153	2017
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	118	2013
RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle Advances in Neural Information Processing Systems, 2175-2183, 2013	105	2013
Advances in Markov chain Monte Carlo methods I Murray University College London, 2007	100	2007
Bayesian learning in undirected graphical models: approximate MCMC algorithms I Murray, Z Ghahramani Proceedings of the 20th conference on Uncertainty in artificial intelligence …, 2004	100	2004
A deep and tractable density estimator B Uria, I Murray, H Larochelle Proceedings of The 31st International Conference on Machine Learning, JMLR W …, 2014	94	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	91*	2010
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	87	2016
Evaluating probabilities under high-dimensional latent variable models I Murray, RR Salakhutdinov Advances in Neural Information Processing Systems 21, 1137-1144, 2009	87	2009
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	72	2016
Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016	68	2016
The Gaussian process density sampler RP Adams, I Murray, DJC MacKay Advances in Neural Information Processing Systems 21, 9-16, 2009	61*	2009

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