Iain Murray

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George PapamakariosDeepMindAdresse e-mail validée de google.com
Ryan P. AdamsPrinceton UniversityAdresse e-mail validée de princeton.edu
David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeAdresse e-mail validée de mrao.cam.ac.uk
Hugo LarochelleGoogle Brain & MilaAdresse e-mail validée de google.com
Conor DurkanDeepMindAdresse e-mail validée de deepmind.com
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUAdresse e-mail validée de cs.cmu.edu
Artur BekasovAmazonAdresse e-mail validée de amazon.com
Benigno UríaGoogle DeepMindAdresse e-mail validée de google.com
Steve RenalsUniversity of EdinburghAdresse e-mail validée de ed.ac.uk
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleAdresse e-mail validée de eng.cam.ac.uk
Ben KrauseResearch Scientist, SalesforceAdresse e-mail validée de salesforce.com
Theo PavlakouPhD student, The University of EdinburghAdresse e-mail validée de ed.ac.uk
Mathieu GermainResearcher @ MilaAdresse e-mail validée de usherbrooke.ca
David MimnoAssociate Professor, Cornell UniversityAdresse e-mail validée de cornell.edu
Hanna WallachPartner Research Manager, Microsoft ResearchAdresse e-mail validée de dirichlet.net
Chris WilliamsProfessor of Machine Learning, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Asa Cooper SticklandPhD student, University of EdinburghAdresse e-mail validée de ed.ac.uk
Liang LuStaff Speech Scientist, Otter.aiAdresse e-mail validée de ttic.edu
Emmanuel KahembweVDE, The University of Edinburgh, Heriot-Watt University & The Edinburgh Centre for RoboticsAdresse e-mail validée de ed.ac.uk
Marc-Alexandre CôtéMicrosoft ResearchAdresse e-mail validée de usherbrooke.ca

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Iain Murray

School of Informatics, University of Edinburgh and Amazon

Adresse e-mail validée de ed.ac.uk - Page d'accueil

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


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009	1106	2009
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in neural information processing systems 30, 2017	943	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	688	2015
The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the fourteenth international conference on artificial …, 2011	616	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	538	2008
MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006	498*	2006
Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010	476	2010
Neural Spline Flows C Durkan, A Bekasov, I Murray, G Papamakarios arXiv preprint arXiv:1906.04032, 2019	404	2019
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	296	2016
Fast ε-free inference of simulation models with bayesian conditional density estimation G Papamakarios, I Murray Advances in neural information processing systems 29, 2016	261	2016
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	255	2009
Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams Advances in neural information processing systems 23, 2010	251	2010
RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle Advances in Neural Information Processing Systems 26, 2013	236	2013
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	209	2019
Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016	207	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	185	2013
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning AC Stickland, I Murray arXiv preprint arXiv:1902.02671, 2019	169	2019
A deep and tractable density estimator B Uria, I Murray, H Larochelle Proceedings of The 31st International Conference on Machine Learning, JMLR W …, 2014	163	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	128*	2010
Dynamic evaluation of neural sequence models B Krause, E Kahembwe, I Murray, S Renals International Conference on Machine Learning, 2766-2775, 2018	127	2018

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