Iain Murray

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Medförfattare

David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeVerifierad e-postadress på mrao.cam.ac.uk
Ryan P. AdamsPrinceton UniversityVerifierad e-postadress på princeton.edu
George PapamakariosDeepMindVerifierad e-postadress på google.com
Hugo LarochelleGoogle Brain & MilaVerifierad e-postadress på google.com
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerifierad e-postadress på cs.cmu.edu
Benigno UríaGoogle DeepMindVerifierad e-postadress på google.com
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerifierad e-postadress på eng.cam.ac.uk
Steve RenalsProfessor of Speech Technology, University of EdinburghVerifierad e-postadress på ed.ac.uk
Ben KrauseResearch Scientist, SalesforceVerifierad e-postadress på salesforce.com
Theo PavlakouPhD student, The University of EdinburghVerifierad e-postadress på ed.ac.uk
Conor DurkanUniversity of EdinburghVerifierad e-postadress på ed.ac.uk
David MimnoAssociate Professor, Cornell UniversityVerifierad e-postadress på cornell.edu
Hanna WallachSenior Principal Researcher, Microsoft ResearchVerifierad e-postadress på dirichlet.net
Mathieu GermainResearcher @ MilaVerifierad e-postadress på usherbrooke.ca
Artur BekasovUniversity of EdinburghVerifierad e-postadress på ed.ac.uk
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerifierad e-postadress på inf.ed.ac.uk
Liang LuSenior Applied Scientist, MicrosoftVerifierad e-postadress på microsoft.com
Asa Cooper SticklandPhD student, University of EdinburghVerifierad e-postadress på ed.ac.uk
George E. DahlGoogle BrainVerifierad e-postadress på cs.toronto.edu
Emmanuel KahembweDoctoral Candidate, The University of Edinburgh, Heriot-Watt University & The Edinburgh Centre forVerifierad e-postadress på ed.ac.uk

Iain Murray

School of Informatics, University of Edinburgh

Verifierad e-postadress på ed.ac.uk - Startsida

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


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009	869	2009
The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the Fourteenth International Conference on Artificial …, 2011	482	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	477	2008
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray arXiv preprint arXiv:1705.07057, 2017	417	2017
MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006	412*	2006
Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010	381	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	377	2015
Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams arXiv preprint arXiv:1006.0868, 2010	218	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	213	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	179	2016
RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle arXiv preprint arXiv:1306.0186, 2013	158	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	143	2013
A deep and tractable density estimator B Uria, I Murray, H Larochelle Proceedings of The 31st International Conference on Machine Learning, JMLR W …, 2014	128	2014
Fast -free Inference of Simulation Models with Bayesian Conditional Density Estimation G Papamakarios, I Murray arXiv preprint arXiv:1605.06376, 2016	124	2016
Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016	118	2016
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	108*	2010
Bayesian learning in undirected graphical models: approximate MCMC algorithms I Murray, Z Ghahramani arXiv preprint arXiv:1207.4134, 2012	106	2012
Advances in Markov chain Monte Carlo methods I Murray University College London, 2007	106	2007
Neural Spline Flows C Durkan, A Bekasov, I Murray, G Papamakarios arXiv preprint arXiv:1906.04032, 2019	102	2019
Evaluating probabilities under high-dimensional latent variable models I Murray, RR Salakhutdinov Advances in Neural Information Processing Systems 21, 1137-1144, 2009	95	2009

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