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 RobinsonSpeechmaticsVerified 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	15181	2003
Information theory and reliable communication RG Gallager Wiley, 1968	10437	1968
Bayesian interpolation DJC MacKay Neural computation 4 (3), 415-447, 1992	6044	1992
Good error-correcting codes based on very sparse matrices DJC MacKay IEEE transactions on Information Theory 45 (2), 399-431, 1999	5896	1999
Near Shannon limit performance of low density parity check codes DJC MacKay, RM Neal Electronics letters 33 (6), 457-458, 1997	5255	1997
A practical Bayesian framework for backpropagation networks DJC MacKay Neural computation 4 (3), 448-472, 1992	4150	1992
Sustainable Energy-without the hot air DJC MacKay Bloomsbury Publishing, 2016	2640	2016
Low density parity check codes over GF (q) MC Davey, DJC MacKay 1998 Information Theory Workshop (Cat. No. 98EX131), 70-71, 1998	1788	1998
Information-based objective functions for active data selection DJC MacKay Neural computation 4 (4), 590-604, 1992	1755	1992
Fountain codes DJC MacKay IEE Proceedings-Communications 152 (6), 1062-1068, 2005	1655	2005
Introduction to Gaussian processes DJC MacKay NATO ASI series F computer and systems sciences 168, 133-166, 1998	1431	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	1306	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	1260	1998
Bayesian methods for adaptive models DJC Mackay California Institute of Technology, 1992	1107	1992
Bayesian online changepoint detection RP Adams, DJC MacKay arXiv preprint arXiv:0710.3742, 2007	1104	2007
The evidence framework applied to classification networks DJC MacKay Neural computation 4 (5), 720-736, 1992	1029	1992
Introduction to monte carlo methods DJC Mackay Learning in graphical models, 175-204, 1998	773	1998
Good codes based on very sparse matrices DJC MacKay, RM Neal IMA International Conference on Cryptography and Coding, 100-111, 1995	720	1995
Bayesian nonlinear modeling for the prediction competition DJC MacKay ASHRAE transactions 100 (2), 1053-1062, 1994	662	1994
The role of constraints in Hebbian learning KD Miller, DJC MacKay Neural computation 6 (1), 100-126, 1994	616	1994

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