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
Hugo LarochelleGoogle BrainVerifierad e-postadress på usherbrooke.ca
George PapamakariosDeepMindVerifierad 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 Chief Scientist, UberVerifierad 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
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
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerifierad e-postadress på inf.ed.ac.uk
George E. DahlGoogle BrainVerifierad e-postadress på cs.toronto.edu
peter lathamProfessor, Gatsby Computational Neuroscience Unit, UCLVerifierad e-postadress på gatsby.ucl.ac.uk
Krzysztof ChalupkaCalifornia Institute of TechnologyVerifierad e-postadress på caltech.edu
Robert NishiharaPhD student, UC BerkeleyVerifierad e-postadress på eecs.berkeley.edu
David W HoggNew York University & MPIA & Center for Computational Astrophysics, Flatiron InstituteVerifierad e-postadress på nyu.edu
Jakob MackeMachine Learning in Science, Tübingen UniversityVerifierad e-postadress på uni-tuebingen.de
Korin RichmondCentre for Speech Technology Research, University of EdinburghVerifierad e-postadress på cstr.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	832	2009
The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the Fourteenth International Conference on Artificial …, 2011	442	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	442	2008
MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006	398*	2006
Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010	361	2010
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in Neural Information Processing Systems, 2338-2347, 2017	340	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	319	2015
Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams Advances in neural information processing systems, 1732-1740, 2010	208	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	201	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	155	2016
RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle Advances in Neural Information Processing Systems, 2175-2183, 2013	142	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	134	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	118	2014
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	106	2016
Advances in Markov chain Monte Carlo methods I Murray University College London, 2007	106	2007
Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016	104	2016
Bayesian learning in undirected graphical models: approximate MCMC algorithms I Murray, Z Ghahramani arXiv preprint arXiv:1207.4134, 2012	104	2012
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	102*	2010
Evaluating probabilities under high-dimensional latent variable models I Murray, RR Salakhutdinov Advances in Neural Information Processing Systems 21, 1137-1144, 2009	95	2009
Dynamic evaluation of neural sequence models B Krause, E Kahembwe, I Murray, S Renals International Conference on Machine Learning, 2766-2775, 2018	76	2018

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