David Barber
David Barber
Department of Computer Science, University College London
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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
The IM algorithm: a variational approach to information maximization
DBF Agakov
Advances in neural information processing systems 16 (320), 201, 2004
A scalable laplace approximation for neural networks
H Ritter, A Botev, D Barber
6th International Conference on Learning Representations, ICLR 2018 …, 2018
Bayesian time series models
D Barber, AT Cemgil, S Chiappa
Cambridge University Press, 2011
Thinking fast and slow with deep learning and tree search
T Anthony, Z Tian, D Barber
Advances in neural information processing systems 30, 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
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting
H Ritter, A Botev, D Barber
Neural Information Processing Systems, 2018
Ensemble learning in Bayesian neural networks
D Barber, CM Bishop
Nato ASI Series F Computer and Systems Sciences 168, 215-238, 1998
Practical gauss-newton optimisation for deep learning
A Botev, H Ritter, D Barber
International Conference on Machine Learning, 557-565, 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
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
Thermodynamics of rock deformation by pressure solution
DJ Barber, PG Meredith, FK Lehner
Deformation processes in minerals, ceramics and rocks, 296-333, 1990
Practical Lossless Compression with Latent Variables using Bits Back Coding
james townsend
Modular networks: Learning to decompose neural computation
L Kirsch, J Kunze, D Barber
Advances in neural information processing systems 31, 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
Ensemble learning for multi-layer networks
D Barber, C Bishop
Advances in neural information processing systems 10, 1997
Gaussian processes for Bayesian classification via hybrid Monte Carlo
D Barber, C Williams
Advances in neural information processing systems 9, 1996
Expectation correction for smoothed inference in switching linear dynamical systems.
D Barber
Journal of Machine Learning Research 7 (11), 2006
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
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Articles 1–20