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Number of items: 78.

Article

(2013) Optimization by variational bounding. ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning pp. 473-478.

Barber, D; (2012) Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices. CoRR , abs/12

Barber, D; (2009) Identifying graph clusters using variational inference and links to covariance parametrization. PHILOS T R SOC A , 367 (1906) 4407 - 4426. 10.1098/rsta.2009.0117.

Barber, D; (2008) Clique matrices for statistical graph decomposition and parameterising restricted positive definite matrices. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008 pp. 26-33.

Barber, D; (2006) Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems. Journal of Machine Learning Research , 7 pp. 2515-2540. Gold open access

Barber, D; (1997) OhioLINK: A Consortial Approach to Digital Library Management. D-Lib Magazine , 3 (4)

Barber, D; Cemgil, AT; (2010) Graphical Models for Time-Series. IEEE SIGNAL PROC MAG , 27 (6) 18 - 28. 10.1109/MSP.2010.938028.

Barber, D; Laar, PVD; (2011) Variational Cumulant Expansions for Intractable Distributions. CoRR , abs/11

Barber, D; Laar, PVD; (1999) Variational Cumulant Expansions for Intractable Distributions. J. Artif. Intell. Res. , 10 pp. 435-455.

Barber, D.; (2006) Expectation correction for smoothed inference in switching linear dynamical systems. Journal of Machine Learning Research (7) pp. 2515-2540. Gold open access

Barratt, D.C.; Davies, A.H.; Hughes, A.D.; Thom, S.A.; Humphries, K.N.; (2001) Accuracy of an electromagnetic three-dimensional ultrasound system for carotid artery imaging. Ultrasound in Medicine & Biology , 27 (10) pp. 1421-1425. 10.1016/S0301-5629(01)00447-1.

Cemgil, AT; Kappen, HJ; Barber, D; (2006) A generative model for music transcription. IEEE Trans. Audio, Speech & Language Processing , 14 (2) pp. 679-694. Green open access
file

Challis, E; Barber, D; (2013) Gaussian Kullback-Leibler Approximate Inference. JOURNAL OF MACHINE LEARNING RESEARCH , 14 pp. 2239-2286. Gold open access

Challis, E; Barber, D; (2012) Affine independent variational inference. Advances in Neural Information Processing Systems , 3 pp. 2186-2194.

Challis, E; Barber, D; (2012) Affine Independent Variational Inference. Neural Information Processing Systems 2195-2203-2195-2203.

Chiappa, S; Barber, D; (2007) Output grouping using Dirichlet Mixtures of Linear Gaussian State-Space models. PROCEEDINGS OF THE 5TH INTERNATIONAL SYMPOSIUM ON IMAGE AND SIGNAL PROCESSING AND ANALYSIS 446-+.

Chiappa, S; Barber, D; (2007) Bayesian factorial linear Gaussian state-space models for biosignal decomposition. IEEE Signal Processing Letters , 14 (4) pp. 267-270. Green open access
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Chiappa, S; Barber, D; (2006) EEG Classification using Generative Independent Component Analysis. Neurocomputing , 69 (7-9) pp. 769-777. 10.1016/j.neucom.2005.12.028.

Chiappa, S; Barber, D; (2006) EEG classification using generative independent component analysis. Neurocomputing , 69 (7-9) pp. 769-777.

Furmston, T; Barber, D; (2012) Efficient Inference in Markov Control Problems. CoRR , abs/12

Furmston, T; Barber, D; (2011) Effcient inference in Markov control problems. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011 pp. 221-229.

Furmston, T; Barber, D; (2011) Lagrange Dual Decomposition for Finite Horizon Markov Decision Processes. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT I , 6911 pp. 487-502.

Furmston, T; Barber, D; (2010) Variational methods for reinforcement learning. Journal of Machine Learning Research , 9 pp. 241-248. Gold open access

Furmston, T; Lever, G; Barber, D; (2016) Approximate Newton Methods for Policy Search in Markov Decision Processes. Journal of Machine Learning Research , 17 , Article 227. Green open access
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Mesot, B; Barber, D; (2009) A simple alternative derivation of the expectation correction algorithm. IEEE Signal Processing Letters , 16 (2) pp. 121-124. 10.1109/LSP.2008.2008569.

Mesot, B; Barber, D; (2009) A Simple Alternative Derivation of the Expectation Correction Algorithm. IEEE SIGNAL PROC LET , 16 (1-3) 121 - 124. 10.1109/LSP.2008.2008569.

Mesot, B; Barber, D; (2007) Switching Linear Dynamical Systems for Noise Robust Speech Recognition. IEEE Trans. Audio, Speech & Language Processing , 15 (6) pp. 1850-1858.

Pfister, J-P; Toyoizumi, T; Barber, D; Gerstner, W; (2006) Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. Neural Computation , 18 (6) pp. 1318-1348.

Sollich, P; Barber, D; (1998) Online Learning from Finite Training Sets and Robustness to Input Bias. Neural Computation , 10 (8) pp. 2201-2217.

Staines, J; Barber, D; (2012) Variational Optimization. CoRR , abs/12

Vlassis, N; Littman, ML; Barber, D; (2012) On the computational complexity of stochastic controller optimization in POMDPs. ACM Transactions on Computation Theory , 4 (4) 10.1145/2382559.2382563.

Vlassis, N; Littman, ML; Barber, D; (2012) On the Computational Complexity of Stochastic Controller Optimization in POMDPs. TOCT , 4 (4) 12:1-12:1.

Westerdijk, M; Barber, D; Wiegerinck, W; (2001) Deterministic Generative Models for Fast Feature Discovery. Data Min. Knowl. Discov. , 5 (4) pp. 337-363.

Williams, CKI; Barber, D; (1998) Bayesian Classification With Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. , 20 (12) pp. 1342-1351. 10.1109/34.735807.

Wu, X; Kim, GH; Salisbury, ML; Barber, D; Bartholmai, BJ; Brown, KK; Conoscenti, CS; ... Walsh, SLF; + view all (2019) Computed Tomographic Biomarkers in Idiopathic Pulmonary Fibrosis: The Future of Quantitative Analysis. American Journal of Respiratory and Critical Care Medicine , 199 (1) pp. 12-21. 10.1164/rccm.201803-0444PP. Green open access
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Book

UNSPECIFIED (Ed). (2011) Bayesian time series models. (Vol.978052).

Book chapter

(2011) Approximate inference in switching linear dynamical systems using Gaussian mixtures. In: Bayesian Time Series Models. (pp. 166-181).

(2011) Inference and estimation in probabilistic time series models. In: Bayesian Time Series Models. (pp. 1-31).

Barber, D; Bishop, CM; (1998) Ensemble learning in Bayesian neural networks. In: UNSPECIFIED (pp. 215-238). Springer Verlag

Scutari, M; Strimmer, K; Introduction to Graphical Modelling. In: UNSPECIFIED

Proceedings paper

Agakov, FV; Barber, D; (2005) Auxiliary Variational Information Maximization for Dimensionality Reduction. In: Saunders, C and Grobelnik, M and Gunn, SR and Shawe-Taylor, J, (eds.) (pp. pp. 103-114). Springer

Agakov, FV; Barber, D; (2005) Kernelized Infomax Clustering. In: (pp. pp. 17-24).

Agakov, FV; Barber, D; (2004) An Auxiliary Variational Method. In: Pal, NR and Kasabov, N and Mudi, RK and Pal, S and Parui, SK, (eds.) (pp. pp. 561-566). Springer

Agakov, FV; Barber, D; (2004) Variational Information Maximization for Neural Coding. In: Pal, NR and Kasabov, N and Mudi, RK and Pal, S and Parui, SK, (eds.) (pp. pp. 543-548). Springer

Agakov, FV; Barber, D; (2003) Approximate Learning in Temporal Hidden Hopfield Models. In: Kaynak, O and Alpaydin, E and Oja, E and Xu, L, (eds.) (pp. pp. 107-114). Springer

Anthony, T; Tian, Z; Barber, D; (2017) Thinking Fast and Slow with Deep Learning and Tree Search. In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.) Advances in Neural Information Processing Systems 30 (NIPS 2017) pre-proceedings. NIPS Proceedings: Long Beach, CA, USA. Green open access
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Barber, D; (2003) Learning in spiking neural assemblies. In: (pp. pp. 165-172).

Barber, D; (2002) Dynamic Bayesian Networks with Deterministic Latent Tables. In: Becker, S and Thrun, S and Obermayer, K, (eds.) (pp. pp. 713-720). MIT Press

Barber, D; (2002) Learning in Spiking Neural Assemblies. In: Becker, S and Thrun, S and Obermayer, K, (eds.) (pp. pp. 149-156). MIT Press

Barber, D; Agakov, FV; (2003) The IM Algorithm: A Variational Approach to Information Maximization. In: Thrun, S and Saul, LK and Schölkopf, B, (eds.) (pp. pp. 201-208). MIT Press

Barber, D; Bishop, CM; (1997) Ensemble Learning for Multi-Layer Networks. In: Jordan, MI and Kearns, MJ and Solla, SA, (eds.) (pp. pp. 395-401). The MIT Press

Barber, D; Bishop, CM; (1996) Bayesian Model Comparison by Monte Carlo Chaining. In: Mozer, M and Jordan, MI and Petsche, T, (eds.) (pp. pp. 333-339). MIT Press

Barber, D; Chiappa, S; (2006) Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. In: (Proceedings) 20th Conference on Neural Information Processing Systems (NIPS), 2006. (pp. pp. 1-8). Neural Information Processing System Foundation

Barber, D; Chiappa, S; (2006) Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. In: Schölkopf, B and Platt, JC and Hofmann, T, (eds.) (pp. pp. 81-88). MIT Press

Barber, D; Mesot, B; (2006) A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems. In: Schölkopf, B and Platt, JC and Hofmann, T, (eds.) (pp. pp. 89-96). MIT Press

Barber, D; Saad, D; (1995) Knowledge and generalisation in simple learning systems. In:

Barber, D; Schottky, B; (1997) Radial Basis Functions: A Bayesian Treatment. In: Jordan, MI and Kearns, MJ and Solla, SA, (eds.) (pp. pp. 402-408). The MIT Press

Barber, D; Sollich, P; (1999) Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. In: Solla, SA and Leen, TK and Müller, K-R, (eds.) (pp. pp. 393-399). The MIT Press

Barber, D; Wiegerinck, W; (1998) Tractable Variational Structures for Approximating Graphical Models. In: Kearns, MJ and Solla, SA and Cohn, DA, (eds.) (pp. pp. 183-189). The MIT Press

Barber, D; Williams, CKI; (1996) Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. In: Mozer, M and Jordan, MI and Petsche, T, (eds.) (pp. pp. 340-346). MIT Press

Barber, D.; Chiappa, S.; (2007) Unified inference for variational Bayesian linear Gaussian state-space model. In: Schölkopf, B. and Platt, P. and Hofmann, T., (eds.) Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference. (pp. pp. 81-88). The MIT Press: Cambridge, US.

Botev, A; Lever, G; Barber, D; (2017) Nesterov's accelerated gradient and momentum as approximations to regularised update descent. In: Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN). IEEE: Anchorage, AK, USA. Green open access
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Botev, A; Ritter, J; Barber, D; (2017) Practical Gauss-Newton Optimisation for Deep Learning. In: Precup, D and Teh, YW, (eds.) Proceedings of the 34th International Conference on Machine Learning. (pp. pp. 557-565). Proceedings of Machine Learning Research: Sydney, Australia. Green open access
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Bracegirdle, C; Barber, D; (2012) Bayesian conditional cointegration. In: Proceedings of the 29th International Conference on Machine Learning (ICML 2012). (pp. 1095 - 1102). International Conference on Machine Learning: Edinburgh, UK. Green open access
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Bracegirdle, C; Barber, D; (2011) Switch-reset models: Exact and approximate inference. In: (pp. pp. 190-198). Gold open access

Challis, E; Barber, D; (2011) Concave Gaussian variational approximations for inference in large-scale Bayesian linear models. In: Gordon, G and Dunson, D, (eds.) Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. (pp. 199 - 207). Journal of Machine Learning Research Green open access
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Chiappa, S; Barber, D; (2005) generative independent component analysis for EEG classification. In: (pp. pp. 297-302).

Furmston, T; Barber, D; (2012) A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes. In: Advances in Neural Information Processing Systems 25 (NIPS 2012). (pp. 2726 - 2734). Neural Information Processing Systems Foundation Green open access
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Furmston, T; Barber, D; (2011) Efficient Inference in Markov Control Problems. In: Cozman, FG and Pfeffer, A, (eds.) (pp. pp. 221-229). AUAI Press

He, Z; Gao, S; Xiao, L; Barber, D; (2017) Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.) Advances in Neural Information Processing Systems 30 (NIPS 2017) pre-proceedings. NIPS Proceedings: Long Beach, CA, USA. Green open access
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Innes, M; Karpinski, S; Shah, V; Barber, D; Saito Stenetorp, PLEPS; Besard, T; Bradbury, J; ... Yuret, D; + view all (2018) On Machine Learning and Programming Languages. In: (Proceedings) SysML 2018. Association for Computing Machinery (ACM) (In press). Green open access
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Paiement, J-F; Eck, D; Bengio, S; Barber, D; (2005) A graphical model for chord progressions embedded in a psychoacoustic space. In: Raedt, LD and Wrobel, S, (eds.) (pp. pp. 641-648). ACM

Perrow, M; Barber, D; (2006) Tagging of name records for genealogical data browsing. In: (pp. pp. 316-325).

Pfister, J-P; Barber, D; Gerstner, W; (2003) Optimal Hebbian Learning: A Probabilistic Point of View. In: Kaynak, O and Alpaydin, E and Oja, E and Xu, L, (eds.) (pp. pp. 92-98). Springer

Shah, H; Barber, D; Botev, A; (2017) Overdispersed Variational Autoencoders. In: 2017 International Joint Conference on Neural Networks (IJCNN). (pp. pp. 1109-1116). IEEE Green open access
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Shah, H; Zheng, B; Barber, D; (2018) Generating Sentences Using a Dynamic Canvas. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). AAAI Association for the Advancement of Artificial Intelligence Green open access
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Sollich, P; Barber, D; (1997) On-line Learning from Finite Training Sets in Nonlinear Networks. In: Jordan, MI and Kearns, MJ and Solla, SA, (eds.) (pp. pp. 357-363). The MIT Press

Sollich, P; Barber, D; (1996) Online Learning from Finite Training Sets: An Analytical Case Study. In: Mozer, M and Jordan, MI and Petsche, T, (eds.) (pp. pp. 274-280). MIT Press

This list was generated on Sun Apr 7 18:35:53 2019 BST.