Iain Murray

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Referințe bibliografice	10627	7823
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George PapamakariosGoogle DeepMindAdresă de e-mail confirmată pe google.com
Ryan P. AdamsPrinceton UniversityAdresă de e-mail confirmată pe princeton.edu
David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeAdresă de e-mail confirmată pe mrao.cam.ac.uk
Hugo LarochelleGoogle DeepMind & MilaAdresă de e-mail confirmată pe google.com
Artur BekasovAmazonAdresă de e-mail confirmată pe amazon.com
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUAdresă de e-mail confirmată pe cs.cmu.edu
Benigno UríaGoogle DeepMindAdresă de e-mail confirmată pe google.com
Steve RenalsUniversity of EdinburghAdresă de e-mail confirmată pe ed.ac.uk
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleAdresă de e-mail confirmată pe eng.cam.ac.uk
Theo PavlakouPhD student, The University of EdinburghAdresă de e-mail confirmată pe ed.ac.uk
Ben KrauseSenior Research Scientist, SalesforceAdresă de e-mail confirmată pe salesforce.com
Mathieu GermainResearcher @ MilaAdresă de e-mail confirmată pe usherbrooke.ca
David MimnoAssociate Professor, Cornell UniversityAdresă de e-mail confirmată pe cornell.edu
Hanna WallachPartner Research Manager, Microsoft ResearchAdresă de e-mail confirmată pe dirichlet.net
Asa Cooper SticklandPhD student, University of EdinburghAdresă de e-mail confirmată pe ed.ac.uk
Chris WilliamsProfessor of Machine Learning, University of EdinburghAdresă de e-mail confirmată pe inf.ed.ac.uk
Liang LuStaff Speech Scientist, Otter.aiAdresă de e-mail confirmată pe ttic.edu
Emmanuel KahembweVDE, The University of Edinburgh, Heriot-Watt University & The Edinburgh Centre for RoboticsAdresă de e-mail confirmată pe ed.ac.uk
Marc-Alexandre CôtéMicrosoft ResearchAdresă de e-mail confirmată pe usherbrooke.ca
George E. DahlGoogle Inc.Adresă de e-mail confirmată pe google.com

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Iain Murray

School of Informatics, University of Edinburgh and Amazon

Adresă de e-mail confirmată pe ed.ac.uk - Pagina de pornire

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


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009	1189	2009
Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in neural information processing systems 30, 2017	1187	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	818	2015
The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the fourteenth international conference on artificial …, 2011	657	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	570	2008
Neural Spline Flows C Durkan, A Bekasov, I Murray, G Papamakarios arXiv preprint arXiv:1906.04032, 2019	565	2019
MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006	522*	2006
Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010	518	2010
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	344	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	320	2016
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	275	2021
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	273	2009
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	267	2019
Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams Advances in neural information processing systems 23, 2010	261	2010
RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle Advances in Neural Information Processing Systems 26, 2013	257	2013
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning AC Stickland, I Murray arXiv preprint arXiv:1902.02671, 2019	232	2019
Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016	225	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	199	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	173	2014
Dynamic evaluation of neural sequence models B Krause, E Kahembwe, I Murray, S Renals International Conference on Machine Learning, 2766-2775, 2018	136	2018

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