David Barber
David Barber
Department of Computer Science, University College London
Verified email at ucl.ac.uk
Cited by
Cited by
Bayesian reasoning and machine learning
D Barber
Cambridge University Press, 2012
Bayesian classification with Gaussian processes
CKI Williams, D Barber
IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (12), 1342 …, 1998
Bayesian time series models
D Barber, AT Cemgil, S Chiappa
Cambridge University Press, 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
The im algorithm: a variational approach to information maximization
D Barber, FV Agakov
Advances in neural information processing systems, None, 2003
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
Thinking fast and slow with deep learning and tree search
T Anthony, Z Tian, D Barber
Advances in Neural Information Processing Systems, 5360-5370, 2017
Ensemble learning in Bayesian neural networks
D Barber, CM Bishop
Nato ASI Series F Computer and Systems Sciences 168, 215-238, 1998
Gaussian processes for Bayesian classification via hybrid Monte Carlo
D Barber, C Williams
Advances in neural information processing systems 9, 340-346, 1996
Ensemble learning for multi-layer networks
D Barber, C Bishop
Advances in neural information processing systems 10, 395-401, 1997
Expectation correction for smoothed inference in switching linear dynamical systems
D Barber
Journal of Machine Learning Research 7 (Nov), 2515-2540, 2006
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting
H Ritter, A Botev, D Barber
Neural Information Processing Systems, 2018
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
A scalable laplace approximation for neural networks
H Ritter, A Botev, D Barber
6th International Conference on Learning Representations, ICLR 2018 …, 2018
Graphical models for time-series
D Barber, AT Cemgil
IEEE Signal Processing Magazine 27 (6), 18-28, 2010
Practical gauss-newton optimisation for deep learning
A Botev, H Ritter, D Barber
arXiv preprint arXiv:1706.03662, 2017
Generative model based polyphonic music transcription
AT Cemgil, B Kappen, D Barber
2003 IEEE Workshop on Applications of Signal Processing to Audio and …, 2003
Gaussian kullback-leibler approximate inference
E Challis, D Barber
The Journal of Machine Learning Research 14 (1), 2239-2286, 2013
An auxiliary variational method
FV Agakov, D Barber
International Conference on Neural Information Processing, 561-566, 2004
Gaussian processes for Bayesian estimation in ordinary differential equations
D Barber, Y Wang
International conference on machine learning, 1485-1493, 2014
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