David Barber

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	All	Since 2016
Citations	6044	3499
h-index	31	25
i10-index	60	38

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Silvia ChiappaSenior Staff Research Scientist, DeepMindVerified email at google.com
Ali Taylan CemgilResearcher, DeepMind UK, Prof. (on leave) from Bogazici University, Istanbul, TurkeyVerified email at boun.edu.tr
Aleksandar BotevGoogle DeepmindVerified email at google.com
Peter SollichProfessor of Non-Equilibrium Statistical PhysicsVerified email at uni-goettingen.de
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Hilbert Johan KappenRadboud UniversityVerified email at science.ru.nl
Wulfram GERSTNERProfessor of Computer Science, EPFL, Brain Mind Institute, Ecole Polytechnique Federale de LausanneVerified email at epfl.ch
Jean-Pascal PfisterProfessor of Theoretical Neuroscience, Institute of Neuroinformatics, University of Zurich and ETHVerified email at ini.uzh.ch
Christopher M. BishopDistinguished Scientist, Microsoft Research, Cambridge, U.K.Verified email at microsoft.com
Thomas BirdUniversity College LondonVerified email at ucl.ac.uk
Taro ToyoizumiRIKEN Center for Brain ScienceVerified email at brain.riken.jp
Julius KunzeUniversity College LondonVerified email at ucl.ac.uk
Raza HabibUCLVerified email at ucl.ac.uk
Thomas William AnthonyGoogle DeepMindVerified email at google.com
Zheng TianUniversity College LondonVerified email at ucl.ac.uk
Joe StainesJ.P. Morgan Asset ManagementVerified email at jpmorgan.com
Zhen HeBeijing Institute of Basic Medical Sciences; University College LondonVerified email at ucl.ac.uk
Louis KirschIDSIA, University College LondonVerified email at louiskirsch.com
James TownsendUCL, LondonVerified email at ucl.ac.uk
Nikos VlassisSr Data Scientist, NetflixVerified email at netflix.com

David Barber

Department of Computer Science, University College London

Verified email at ucl.ac.uk

Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Bayesian reasoning and machine learning D Barber Cambridge University Press, 2012	1707	2012
Bayesian classification with Gaussian processes CKI Williams, D Barber IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (12), 1342 …, 1998	852	1998
Bayesian time series models D Barber, AT Cemgil, S Chiappa Cambridge University Press, 2011	258	2011
Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning JP Pfister, T Toyoizumi, D Barber, W Gerstner Neural computation 18 (6), 1318-1348, 2006	258	2006
The im algorithm: a variational approach to information maximization DBF Agakov Advances in neural information processing systems 16, 201, 2004	211	2004
Thinking fast and slow with deep learning and tree search T Anthony, Z Tian, D Barber arXiv preprint arXiv:1705.08439, 2017	168	2017
A generative model for music transcription AT Cemgil, HJ Kappen, D Barber IEEE Transactions on Audio, Speech, and Language Processing 14 (2), 679-694, 2006	161	2006
Ensemble learning in Bayesian neural networks D Barber, CM Bishop Nato ASI Series F Computer and Systems Sciences 168, 215-238, 1998	153	1998
Gaussian processes for Bayesian classification via hybrid Monte Carlo D Barber, CKI Williams Advances in neural information processing systems, 340-346, 1997	104	1997
Ensemble learning for multi-layer networks D Barber, CM Bishop Advances in neural information processing systems, 395-401, 1998	97	1998
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting H Ritter, A Botev, D Barber Neural Information Processing Systems, 2018	93	2018
A scalable laplace approximation for neural networks H Ritter, A Botev, D Barber 6th International Conference on Learning Representations, ICLR 2018 …, 2018	91	2018
Expectation correction for smoothed inference in switching linear dynamical systems. D Barber Journal of Machine Learning Research 7 (11), 2006	87	2006
Switching linear dynamical systems for noise robust speech recognition B Mesot, D Barber IEEE Transactions on Audio, Speech, and Language Processing 15 (6), 1850-1858, 2007	82	2007
Practical gauss-newton optimisation for deep learning A Botev, H Ritter, D Barber International Conference on Machine Learning, 557-565, 2017	78	2017
Graphical models for time-series D Barber, AT Cemgil IEEE Signal Processing Magazine 27 (6), 18-28, 2010	72	2010
An auxiliary variational method FV Agakov, D Barber International Conference on Neural Information Processing, 561-566, 2004	59	2004
Generative model based polyphonic music transcription AT Cemgil, B Kappen, D Barber 2003 IEEE Workshop on Applications of Signal Processing to Audio and …, 2003	56	2003
Gaussian processes for Bayesian estimation in ordinary differential equations D Barber, Y Wang International conference on machine learning, 1485-1493, 2014	55	2014
Gaussian Kullback-Leibler Approximate Inference. E Challis, D Barber Journal of Machine Learning Research 14 (8), 2013	54	2013

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