Iain Murray

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George PapamakariosGoogle DeepMindVerified email at google.com
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Hugo LarochelleGoogle DeepMind & MilaVerified email at google.com
David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeVerified email at mrao.cam.ac.uk
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
Theo PavlakouPhD student, The University of EdinburghVerified email at ed.ac.uk
Ben KrauseCo-founder of Psyfy AIVerified email at psyfy.ai
Mathieu GermainResearcher @ MilaVerified email at usherbrooke.ca
Asa Cooper SticklandPostdoctoral Researcher, New York UniversityVerified email at ed.ac.uk
David MimnoAssociate Professor, Cornell UniversityVerified email at cornell.edu
Hanna WallachPartner Research Manager, Microsoft ResearchVerified email at dirichlet.net
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Liang LuStaff Speech Scientist, Otter.aiVerified email at otter.ai
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
George E. DahlGoogle Inc.Verified email at google.com

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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in neural information processing systems 30, 2017	1605	2017
Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009	1310	2009
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	1043	2015
Neural Spline Flows C Durkan, A Bekasov, I Murray, G Papamakarios arXiv preprint arXiv:1906.04032, 2019	867	2019
The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the fourteenth international conference on artificial …, 2011	718	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	599	2008
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	594	2021
Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010	579	2010
MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006	560*	2006
Fast ε-free inference of simulation models with bayesian conditional density estimation G Papamakarios, I Murray Advances in neural information processing systems 29, 2016	460	2016
Neural autoregressive distribution estimation B Uria, MA Côté, K Gregor, I Murray, H Larochelle Journal of Machine Learning Research 17 (205), 1-37, 2016	425	2016
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	400	2019
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning AC Stickland, I Murray arXiv preprint arXiv:1902.02671, 2019	309	2019
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	292	2009
Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams Advances in neural information processing systems 23, 2010	283	2010
RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle Advances in Neural Information Processing Systems 26, 2013	276	2013
Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016	262	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	228	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	197	2014
Dynamic evaluation of neural sequence models B Krause, E Kahembwe, I Murray, S Renals International Conference on Machine Learning, 2766-2775, 2018	148	2018

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