Iain Murray

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George PapamakariosDeepMindVerified email at google.com
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeVerified email at mrao.cam.ac.uk
Hugo LarochelleGoogle Brain & MilaVerified email at google.com
Conor DurkanDeepMindVerified email at deepmind.com
Artur BekasovAmazonVerified email at amazon.com
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Benigno UríaGoogle DeepMindVerified email at google.com
Steve RenalsUniversity of EdinburghVerified email at ed.ac.uk
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Ben KrauseResearch Scientist, SalesforceVerified email at salesforce.com
Theo PavlakouPhD student, The University of EdinburghVerified email at ed.ac.uk
Mathieu GermainResearcher @ MilaVerified email at usherbrooke.ca
David MimnoAssociate Professor, Cornell UniversityVerified email at cornell.edu
Hanna WallachPartner Research Manager, Microsoft ResearchVerified email at dirichlet.net
Asa Cooper SticklandPhD student, University of EdinburghVerified email at ed.ac.uk
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Liang LuStaff Speech Scientist, Otter.aiVerified email at ttic.edu
Emmanuel KahembweVDE, The University of Edinburgh, Heriot-Watt University & The Edinburgh Centre for RoboticsVerified email at ed.ac.uk
Marc-Alexandre CôtéMicrosoft ResearchVerified email at usherbrooke.ca

Iain Murray

School of Informatics, University of Edinburgh and Amazon

Verified email at ed.ac.uk - Homepage

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


Title Sort by citations Sort by year Sort by title	Cited by 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	1127	2009
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in neural information processing systems 30, 2017	1021	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	721	2015
The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the fourteenth international conference on artificial …, 2011	630	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	545	2008
MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006	505*	2006
Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010	486	2010
Neural Spline Flows C Durkan, A Bekasov, I Murray, G Papamakarios arXiv preprint arXiv:1906.04032, 2019	447	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	313	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	273	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	262	2009
Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams Advances in neural information processing systems 23, 2010	257	2010
RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle Advances in Neural Information Processing Systems 26, 2013	245	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	224	2019
Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016	213	2016
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning AC Stickland, I Murray arXiv preprint arXiv:1902.02671, 2019	192	2019
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	191	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	164	2014
Maximum likelihood training of score-based diffusion models Y Song, C Durkan, I Murray, S Ermon Advances in Neural Information Processing Systems 34, 1415-1428, 2021	153	2021
Dynamic evaluation of neural sequence models B Krause, E Kahembwe, I Murray, S Renals International Conference on Machine Learning, 2766-2775, 2018	130	2018

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