Iain Murray - Google Scholar Citations

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	All	Since 2015
Citations	4692	3528
h-index	28	26
i10-index	40	35

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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
George PapamakariosDeepMindVerified 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 Chief Scientist, UberVerified email at eng.cam.ac.uk
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 MimnoAssociate 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
George E. DahlGoogle BrainVerified email at cs.toronto.edu
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
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, 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


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Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009	736	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	413	2008
The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the Fourteenth International Conference on Artificial …, 2011	381	2011
MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006	367*	2006
Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010	334	2010
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in Neural Information Processing Systems, 2338-2347, 2017	224	2017
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	222	2015
Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams Advances in neural information processing systems, 1732-1740, 2010	198	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	184	2009
RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle Advances in Neural Information Processing Systems, 2175-2183, 2013	120	2013
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
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	108	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	105	2014
Advances in Markov chain Monte Carlo methods I Murray University College London, 2007	103	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
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	94*	2010
Evaluating probabilities under high-dimensional latent variable models I Murray, RR Salakhutdinov Advances in Neural Information Processing Systems 21, 1137-1144, 2009	90	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	83	2016
Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016	73	2016
The Gaussian process density sampler RP Adams, I Murray, DJC MacKay Advances in Neural Information Processing Systems 21, 9-16, 2009	66*	2009

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