David Barber
David Barber
Department of Computer Science, University College London
Adresse e-mail validée de ucl.ac.uk
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Bayesian reasoning and machine learning
D Barber
Cambridge University Press, 2012
18772012
Bayesian classification with Gaussian processes
CKI Williams, D Barber
IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (12), 1342 …, 1998
8991998
Bayesian time series models
D Barber, AT Cemgil, S Chiappa
Cambridge University Press, 2011
2812011
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
2702006
The IM algorithm: a variational approach to information maximization
DBF Agakov
Advances in neural information processing systems 16 (320), 201, 2004
2642004
Thinking fast and slow with deep learning and tree search
T Anthony, Z Tian, D Barber
arXiv preprint arXiv:1705.08439, 2017
2122017
Ensemble learning in Bayesian neural networks
D Barber, CM Bishop
Nato ASI Series F Computer and Systems Sciences 168, 215-238, 1998
1821998
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
1622006
A scalable laplace approximation for neural networks
H Ritter, A Botev, D Barber
6th International Conference on Learning Representations, ICLR 2018 …, 2018
1432018
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting
H Ritter, A Botev, D Barber
Neural Information Processing Systems, 2018
1342018
Gaussian processes for Bayesian classification via hybrid Monte Carlo
D Barber, CKI Williams
Advances in neural information processing systems, 340-346, 1997
1071997
Ensemble learning for multi-layer networks
D Barber, CM Bishop
Advances in neural information processing systems, 395-401, 1998
1041998
Practical gauss-newton optimisation for deep learning
A Botev, H Ritter, D Barber
International Conference on Machine Learning, 557-565, 2017
1002017
Expectation correction for smoothed inference in switching linear dynamical systems.
D Barber
Journal of Machine Learning Research 7 (11), 2006
932006
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
822007
Graphical models for time-series
D Barber, AT Cemgil
IEEE Signal Processing Magazine 27 (6), 18-28, 2010
752010
An auxiliary variational method
FV Agakov, D Barber
International Conference on Neural Information Processing, 561-566, 2004
672004
Gaussian processes for Bayesian estimation in ordinary differential equations
D Barber, Y Wang
International conference on machine learning, 1485-1493, 2014
662014
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
652017
Gaussian Kullback-Leibler Approximate Inference.
E Challis, D Barber
Journal of Machine Learning Research 14 (8), 2013
642013
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