David Barber

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Silvia ChiappaSenior Staff Research Scientist, Google DeepMind; Honorary Professor, UCLVerified email at google.com
Aleksandar BotevGoogle DeepmindVerified email at google.com
Ali Taylan CemgilResearch Scientist, DeepMind UKVerified email at google.com
Thomas BirdUniversity College LondonVerified email at ucl.ac.uk
Peter SollichProfessor Non-Equilibrium Statistical Physics, Inst. for Theor. Physics, University of GoettingenVerified email at uni-goettingen.de
Mingtian ZhangPhD student, University College LondonVerified email at ucl.ac.uk
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Christopher M. BishopTechnical Fellow, Microsoft Research, Cambridge, U.K.Verified email at microsoft.com
Julius KunzeUniversity College LondonVerified email at ucl.ac.uk
Hilbert Johan KappenRadboud UniversityVerified email at science.ru.nl
Jean-Pascal PfisterProfessor of Theoretical Neuroscience, Institute of Neuroinformatics, University of Zurich and ETHVerified email at ini.uzh.ch
Wulfram GerstnerEPFLVerified email at epfl.ch
Wim WiegerinckResearcher machine learning, Radboud University, NijmegenVerified email at science.ru.nl
Raza HabibUCLVerified email at ucl.ac.uk
James TownsendUniversity of AmsterdamVerified email at uva.nl
David SaadAston UniversityVerified email at aston.ac.uk
Taro ToyoizumiRIKEN Center for Brain ScienceVerified email at riken.jp
Louis KirschThe Swiss AI Lab IDSIAVerified email at idsia.ch
Guy LeverVerified email at google.com
Thomas William AnthonyGoogle DeepMindVerified email at google.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	2331	2012
Bayesian classification with Gaussian processes CKI Williams, D Barber IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998	999	1998
The IM algorithm: a variational approach to information maximization DBF Agakov Advances in neural information processing systems 16 (320), 201, 2004	435*	2004
Bayesian time series models D Barber, AT Cemgil, S Chiappa Cambridge University Press, 2011	365	2011
A scalable laplace approximation for neural networks H Ritter, A Botev, D Barber 6th International Conference on Learning Representations, ICLR 2018 …, 2018	349	2018
Thinking fast and slow with deep learning and tree search T Anthony, Z Tian, D Barber Advances in neural information processing systems 30, 2017	342	2017
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	298	2006
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting H Ritter, A Botev, D Barber Neural Information Processing Systems, 2018	264	2018
Ensemble learning in Bayesian neural networks D Barber, CM Bishop Nato ASI Series F Computer and Systems Sciences 168, 215-238, 1998	262	1998
Practical gauss-newton optimisation for deep learning A Botev, H Ritter, D Barber International Conference on Machine Learning, 557-565, 2017	199	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	176	2006
Nesterov's accelerated gradient and momentum as approximations to regularised update descent A Botev, G Lever, D Barber 2017 International Joint Conference on Neural Networks (IJCNN), 1899-1903, 2017	162	2017
Thermodynamics of rock deformation by pressure solution DJ Barber, PG Meredith, FK Lehner Deformation processes in minerals, ceramics and rocks, 296-333, 1990	149	1990
Practical Lossless Compression with Latent Variables using Bits Back Coding james townsend	124	2019
Ensemble learning for multi-layer networks D Barber, C Bishop Advances in neural information processing systems 10, 1997	117	1997
Computed tomographic biomarkers in idiopathic pulmonary fibrosis. The future of quantitative analysis X Wu, GH Kim, ML Salisbury, D Barber, BJ Bartholmai, KK Brown, ... American journal of respiratory and critical care medicine 199 (1), 12-21, 2019	113	2019
Modular networks: Learning to decompose neural computation L Kirsch, J Kunze, D Barber Advances in neural information processing systems 31, 2018	113	2018
Gaussian processes for Bayesian classification via hybrid Monte Carlo D Barber, C Williams Advances in neural information processing systems 9, 1996	111	1996
Expectation correction for smoothed inference in switching linear dynamical systems. D Barber Journal of Machine Learning Research 7 (11), 2006	107	2006
Graphical models for time-series D Barber, AT Cemgil IEEE Signal Processing Magazine 27 (6), 18-28, 2010	90	2010

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