Radford Neal

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Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoEmail verificata su cs.toronto.edu
Ian H. WittenProfessor of Computer Science, University of Waikato, New ZealandEmail verificata su waikato.ac.nz
David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeEmail verificata su mrao.cam.ac.uk
Sam Roweis (Memorial)New York UniversityEmail verificata su buffalo.edu
Richard ZemelProfessor of Computer Science, University of TorontoEmail verificata su cs.toronto.edu
W.R. PeltierProfessor of Physics, University of TorontoEmail verificata su atmosp.physics.utoronto.ca
Brendan FreyFounder & CEO, Deep Genomics; Co-Founder, Vector Institute; Professor, University of TorontoEmail verificata su psi.toronto.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleEmail verificata su eng.cam.ac.uk
Jennifer ListgartenProfessor, UC Berkeley EECS and Center for Computational BiologyEmail verificata su berkeley.edu
Bradley P. CarlinPhaseV TrialsEmail verificata su counterpointstat.com
Andrew GelmanProfessor of Statistics and Political Science, Columbia UniversityEmail verificata su stat.columbia.edu
Alistair MoffatSchool of Computing and Information Systems, The University of MelbourneEmail verificata su unimelb.edu.au
Susan P HolmesProfessor of Statistics, Stanford UniversityEmail verificata su stat.stanford.edu
Edward MeedsMicrosoft Research CambridgeEmail verificata su microsoft.com
Brian WyvillProfessor of Computer Science, University of Victoria, BCEmail verificata su cs.uvic.ca
Longhai LiUniversity of Saskatchewan, Saskatoon, SK, CANADAEmail verificata su usask.ca
Nicky BestProfessor of Statistics and Epidemiology, Imperial College LondonEmail verificata su imperial.ac.uk
Matthew J. BealChicago Trading CompanyEmail verificata su chicagotrading.com
Celia M.T. GreenwoodSenior Investigator, Lady Davis InstituteEmail verificata su mcgill.ca
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Radford Neal

U Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!

Email verificata su utstat.toronto.edu - Home page

Bayesian inference Monte Carlo methods Information theory Machine learning Neural networks


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Bayesian learning for neural networks RM Neal Springer Science & Business Media, 1995	7073*	1995
Near Shannon limit performance of low density parity check codes DJC MacKay, RM Neal Electronics letters 33 (6), 457-458, 1997	5274	1997
Arithmetic coding for data compression IH Witten, RM Neal, JG Cleary Communications of the ACM 30 (6), 520-540, 1987	4616	1987
MCMC Using Hamiltonian Dynamics R Neal Handbook of Markov Chain Monte Carlo, 113-162, 2011	4595	2011
A view of the EM algorithm that justifies incremental, sparse, and other variants RM Neal, GE Hinton Learning in graphical models, 355-368, 1998	3737	1998
Markov chain sampling methods for Dirichlet process mixture models RM Neal Journal of computational and graphical statistics 9 (2), 249-265, 2000	3304	2000
Slice sampling RM Neal Annals of statistics, 705-741, 2003	2975	2003
Probabilistic inference using Markov chain Monte Carlo methods RM Neal Department of Computer Science, University of Toronto, 1993	2481	1993
Annealed importance sampling RM Neal Statistics and computing 11, 125-139, 2001	1875	2001
The helmholtz machine P Dayan, GE Hinton, RM Neal, RS Zemel Neural computation 7 (5), 889-904, 1995	1767	1995
The" wake-sleep" algorithm for unsupervised neural networks GE Hinton, P Dayan, BJ Frey, RM Neal Science 268 (5214), 1158-1161, 1995	1602	1995
Markov chain Monte Carlo in practice: a roundtable discussion RE Kass, BP Carlin, A Gelman, RM Neal The American Statistician 52 (2), 93-100, 1998	928	1998
Arithmetic coding revisited A Moffat, RM Neal, IH Witten ACM Transactions on Information Systems (TOIS) 16 (3), 256-294, 1998	867	1998
Connectionist learning of belief networks RM Neal Artificial intelligence 56 (1), 71-113, 1992	775	1992
Good codes based on very sparse matrices DJC MacKay, RM Neal IMA International Conference on Cryptography and Coding, 100-111, 1995	723	1995
Regression and classification using Gaussian process priors RM Neal Bayesian Statistics 6, 1998	702*	1998
Monte Carlo implementation of Gaussian process models for Bayesian regression and classification RM Neal arXiv preprint physics/9701026, 1997	644	1997
A split-merge Markov chain Monte Carlo procedure for the Dirichlet process mixture model S Jain, RM Neal Journal of Computational and Graphical Statistics, 2004	628	2004
Priors for infinite networks RM Neal	521	1994
Sampling from multimodal distributions using tempered transitions RM Neal Statistics and computing 6, 353-366, 1996	480	1996

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