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 EdinburghVerified email at ed.ac.uk
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Radford NealU Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!Verified email at utstat.toronto.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
Andrew M. ChildsJoint Center for Quantum Information and Computer Science, University of MarylandVerified email at umd.edu
Dan Witzner HansenProfessor, IT University of CopenhagenVerified email at itu.dk
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	15007	2003
Information theory and reliable communication RG Gallager Wiley, 1968	10401	1968
Bayesian interpolation DJC MacKay Neural computation 4 (3), 415-447, 1992	5992	1992
Good error-correcting codes based on very sparse matrices DJC MacKay IEEE transactions on Information Theory 45 (2), 399-431, 1999	5878	1999
Near Shannon limit performance of low density parity check codes DJC MacKay, RM Neal Electronics letters 33 (6), 457-458, 1997	5215	1997
A practical Bayesian framework for backpropagation networks DJC MacKay Neural computation 4 (3), 448-472, 1992	4094	1992
Sustainable Energy-without the hot air DJC MacKay Bloomsbury Publishing, 2016	2617	2016
Low density parity check codes over GF (q) MC Davey, DJC MacKay 1998 Information Theory Workshop (Cat. No. 98EX131), 70-71, 1998	1785	1998
Information-based objective functions for active data selection DJC MacKay Neural computation 4 (4), 590-604, 1992	1732	1992
Fountain codes DJC MacKay IEE Proceedings-Communications 152 (6), 1062-1068, 2005	1652	2005
Introduction to Gaussian processes DJC MacKay NATO ASI series F computer and systems sciences 168, 133-166, 1998	1410	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	1296	1995
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	1256	1998
Bayesian methods for adaptive models DJC Mackay California Institute of Technology, 1992	1092	1992
Bayesian online changepoint detection RP Adams, DJC MacKay arXiv preprint arXiv:0710.3742, 2007	1079	2007
The evidence framework applied to classification networks DJC MacKay Neural computation 4 (5), 720-736, 1992	1024	1992
Introduction to monte carlo methods DJC Mackay Learning in graphical models, 175-204, 1998	767	1998
Good codes based on very sparse matrices DJC MacKay, RM Neal IMA International Conference on Cryptography and Coding, 100-111, 1995	718	1995
Bayesian nonlinear modeling for the prediction competition DJC MacKay ASHRAE transactions 100 (2), 1053-1062, 1994	657	1994
The role of constraints in Hebbian learning KD Miller, DJC MacKay Neural computation 6 (1), 100-126, 1994	613	1994

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