David Barber

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Aleksandar BotevGoogle DeepmindVerified email at google.com
Silvia ChiappaSenior Staff Research Scientist, Google DeepMind; Honorary Professor, UCLVerified email at google.com
Mingtian ZhangResearch Fellow, University College LondonVerified email at ucl.ac.uk
Ali Taylan CemgilResearch Scientist, DeepMind UKVerified email at google.com
Peter SollichProfessor Non-Equilibrium Statistical Physics, Inst. for Theor. Physics, University of GoettingenVerified email at uni-goettingen.de
Thomas BirdUniversity 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
Wim WiegerinckResearcher machine learning, Radboud University, NijmegenVerified email at science.ru.nl
Hilbert Johan KappenRadboud UniversityVerified email at science.ru.nl
Jean-Pascal PfisterProfessor of Theoretical Neuroscience, Department of Physiology, University of BernVerified email at unibe.ch
Wulfram GerstnerEPFLVerified email at epfl.ch
Joseph JacobUniversity College LondonVerified email at ucl.ac.uk
Edward ChallisCEO, ReinferVerified email at reinfer.io
Raza HabibUCLVerified email at ucl.ac.uk
James TownsendUniversity of AmsterdamVerified email at uva.nl
David SaadAston UniversityVerified email at aston.ac.uk
Guy LeverVerified email at google.com
Taro ToyoizumiRIKEN Center for Brain ScienceVerified email at riken.jp

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	2624	2012
Bayesian classification with Gaussian processes CKI Williams, D Barber IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998	1092	1998
The IM algorithm: a variational approach to information maximization DBF Agakov Advances in neural information processing systems 16 (320), 201, 2004	541*	2004
A scalable laplace approximation for neural networks H Ritter, A Botev, D Barber 6th international conference on learning representations, ICLR 2018 …, 2018	509	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	459	2017
Bayesian time series models D Barber, AT Cemgil, S Chiappa Cambridge University Press, 2011	416	2011
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting H Ritter, A Botev, D Barber Neural Information Processing Systems, 2018	373	2018
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	313	2006
Ensemble learning in Bayesian neural networks D Barber, CM Bishop Nato ASI Series F Computer and Systems Sciences 168, 215-238, 1998	301	1998
Practical Gauss-Newton optimisation for deep learning A Botev, H Ritter, D Barber International Conference on Machine Learning, 557-565, 2017	279	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	185	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	178	2017
Practical Lossless Compression with Latent Variables using Bits Back Coding james townsend	170	2019
Thermodynamics of rock deformation by pressure solution DJ Barber, PG Meredith, FK Lehner Deformation processes in minerals, ceramics and rocks, 296-333, 1990	152	1990
Modular networks: Learning to decompose neural computation L Kirsch, J Kunze, D Barber Advances in neural information processing systems 31, 2018	147	2018
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	141	2019
Ensemble learning for multi-layer networks D Barber, C Bishop Advances in neural information processing systems 10, 1997	122	1997
Gaussian processes for Bayesian classification via hybrid Monte Carlo D Barber, C Williams Advances in neural information processing systems 9, 1996	118	1996
Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms A Koshiyama, E Kazim, P Treleaven, P Rai, L Szpruch, G Pavey, ... Royal Society Open Science 11 (5), 230859, 2024	115	2024
On the computational complexity of stochastic controller optimization in POMDPs N Vlassis, ML Littman, D Barber ACM Transactions on Computation Theory (TOCT) 4 (4), 1-8, 2012	115	2012

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