David MacKay

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H. K. D. H. BhadeshiaProfessor of Metallurgy, Queen Mary University of LondonVerified email at qmul.ac.uk
Iain MurraySchool of Informatics, University of Edinburgh and AmazonVerified email at ed.ac.uk
Radford NealU Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!Verified email at utstat.toronto.edu
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Alan BlackwellProfessor of Interdisciplinary Design, University of CambridgeVerified email at cam.ac.uk
Brendan FreyFounder & CEO, Deep Genomics; Co-Founder, Vector Institute; Professor, University of TorontoVerified email at psi.toronto.edu
Kenneth D. MillerProfessor of Neuroscience, Columbia UniversityVerified email at columbia.edu
Oliver StegleGerman Cancer Research Center (DKFZ) & EMBL HeidelbergVerified email at dkfz.de
Philip John WithersUniversity of ManchesterVerified email at manchester.ac.uk
Tony RobinsonSpeechmatics (Cantab Research Limited)Verified email at tonyRobinson.com
John Paulin HansenProfessor, Technical University of DenmarkVerified email at dtu.dk
Keith VertanenAssociate Professor, Michigan Technological UniversityVerified email at mtu.edu
Thore GraepelGlobal Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCLVerified email at ucl.ac.uk
Dan Witzner HansenProfessor, IT University of CopenhagenVerified email at itu.dk
Andrew M. ChildsJoint Center for Quantum Information and Computer Science, University of MarylandVerified email at umd.edu
Robert MacKayProfessor of Mathematics, University of WarwickVerified email at warwick.ac.uk

David MacKay

Professor of Natural Philosophy, Cavendish Laboratory, University of Cambridge

Verified email at mrao.cam.ac.uk - Homepage

Information Theory and Error-correcting Codes Reliable Computation with Unreliable Hardware Machine Learning and Bayesian Data Modelling Sustainable Energy and Public Understanding of Science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Information theory, inference and learning algorithms DJC MacKay Cambridge university press, 2003	13606	2003
Information theory and reliable communication RG Gallager Wiley, 1968	9533	1968
Good error-correcting codes based on very sparse matrices DJC MacKay IEEE transactions on Information Theory 45 (2), 399-431, 1999	5683	1999
Bayesian interpolation DJC MacKay Neural computation 4 (3), 415-447, 1992	5573	1992
Near Shannon limit performance of low density parity check codes DJC MacKay, RM Neal Electronics letters 33 (6), 457-458, 1997	4959	1997
A practical Bayesian framework for backpropagation networks DJC MacKay Neural computation 4 (3), 448-472, 1992	3658	1992
Sustainable Energy–without the hot air DJC MacKay UIT cambridge, 2008	2473	2008
Low density parity check codes over GF (q) MC Davey, DJC MacKay 1998 Information Theory Workshop (Cat. No. 98EX131), 70-71, 1998	1713	1998
Information-based objective functions for active data selection DJC MacKay Neural computation 4 (4), 590-604, 1992	1594	1992
Fountain codes DJC MacKay IEE Proceedings-Communications 152 (6), 1062-1068, 2005	1492	2005
Introduction to Gaussian processes DJC MacKay NATO ASI series F computer and systems sciences 168, 133-166, 1998	1257	1998
Turbo decoding as an instance of Pearl's" belief propagation" algorithm RJ McEliece, DJC MacKay, JF Cheng IEEE Journal on selected areas in communications 16 (2), 140-152, 1998	1240	1998
Probable networks and plausible predictions-a review of practical Bayesian methods for supervised neural networks DJC MacKay Network: computation in neural systems 6 (3), 469, 1995	1192	1995
Bayesian methods for adaptive models DJC Mackay California Institute of Technology, 1992	1007	1992
The evidence framework applied to classification networks DJC MacKay Neural computation 4 (5), 720-736, 1992	971	1992
Bayesian online changepoint detection RP Adams, DJC MacKay arXiv preprint arXiv:0710.3742, 2007	864	2007
Introduction to monte carlo methods DJC Mackay Learning in graphical models, 175-204, 1998	750	1998
Good codes based on very sparse matrices DJC MacKay, RM Neal Cryptography and Coding: 5th IMA Conference Cirencester, UK, December 18–20 …, 1995	695	1995
Bayesian nonlinear modeling for the prediction competition DJC MacKay ASHRAE transactions 100 (2), 1053-1062, 1994	623	1994
The role of constraints in Hebbian learning KD Miller, DJC MacKay Neural computation 6 (1), 100-126, 1994	576	1994

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