Iain Murray

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George PapamakariosGoogle DeepMindAdresse e-mail validée de google.com
Ryan P. AdamsPrinceton UniversityAdresse e-mail validée de princeton.edu
Hugo LarochelleGoogle DeepMind & MilaAdresse e-mail validée de google.com
David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeAdresse e-mail validée de mrao.cam.ac.uk
Artur BekasovAmazonAdresse e-mail validée de amazon.com
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUAdresse e-mail validée de cs.cmu.edu
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
Theo PavlakouPhD student, The University of EdinburghAdresse e-mail validée de ed.ac.uk
Ben KrauseCo-founder of Psyfy AIAdresse e-mail validée de psyfy.ai
Mathieu GermainResearcher @ MilaAdresse e-mail validée de usherbrooke.ca
Asa Cooper SticklandPostdoctoral Researcher, New York UniversityAdresse e-mail validée de ed.ac.uk
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
Liang LuStaff Speech Scientist, Otter.aiAdresse e-mail validée de otter.ai
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
George E. DahlGoogle Inc.Adresse e-mail validée de google.com

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Iain Murray

School of Informatics, University of Edinburgh

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

Machine Learning Bayesian Inference Hierarchical probabilistic models Markov chain Monte Carlo


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in neural information processing systems 30, 2017	1608	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	868	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	598	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	426	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	277	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	198	2014
Dynamic evaluation of neural sequence models B Krause, E Kahembwe, I Murray, S Renals International Conference on Machine Learning, 2766-2775, 2018	149	2018

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