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David Barber: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Silvia Chiappa, David Barber
    generative independent component analysis for EEG classification. [Citation Graph (0, 0)][DBLP]
    ESANN, 2005, pp:297-302 [Conf]
  2. Felix V. Agakov, David Barber
    Approximate Learning in Temporal Hidden Hopfield Models. [Citation Graph (0, 0)][DBLP]
    ICANN, 2003, pp:107-114 [Conf]
  3. Jean-Pascal Pfister, David Barber, Wulfram Gerstner
    Optimal Hebbian Learning: A Probabilistic Point of View. [Citation Graph (0, 0)][DBLP]
    ICANN, 2003, pp:92-98 [Conf]
  4. Jean-François Paiement, Douglas Eck, Samy Bengio, David Barber
    A graphical model for chord progressions embedded in a psychoacoustic space. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:641-648 [Conf]
  5. Felix V. Agakov, David Barber
    Variational Information Maximization for Neural Coding. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:543-548 [Conf]
  6. Felix V. Agakov, David Barber
    An Auxiliary Variational Method. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:561-566 [Conf]
  7. Mike Perrow, David Barber
    Tagging of name records for genealogical data browsing. [Citation Graph (0, 0)][DBLP]
    JCDL, 2006, pp:316-325 [Conf]
  8. Felix V. Agakov, David Barber
    Kernelized Infomax Clustering. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  9. David Barber
    Learning in Spiking Neural Assemblies. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:149-156 [Conf]
  10. David Barber
    Dynamic Bayesian Networks with Deterministic Latent Tables. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:713-720 [Conf]
  11. David Barber, Felix V. Agakov
    The IM Algorithm: A Variational Approach to Information Maximization. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  12. David Barber, Christopher M. Bishop
    Bayesian Model Comparison by Monte Carlo Chaining. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:333-339 [Conf]
  13. David Barber, Christopher M. Bishop
    Ensemble Learning for Multi-Layer Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  14. David Barber, Bernhard Schottky
    Radial Basis Functions: A Bayesian Treatment. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  15. David Barber, Peter Sollich
    Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:393-399 [Conf]
  16. David Barber, Christopher K. I. Williams
    Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:340-346 [Conf]
  17. David Barber, Wim Wiegerinck
    Tractable Variational Structures for Approximating Graphical Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:183-189 [Conf]
  18. Peter Sollich, David Barber
    Online Learning from Finite Training Sets: An Analytical Case Study. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:274-280 [Conf]
  19. Peter Sollich, David Barber
    On-line Learning from Finite Training Sets in Nonlinear Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  20. Felix V. Agakov, David Barber
    Auxiliary Variational Information Maximization for Dimensionality Reduction. [Citation Graph (0, 0)][DBLP]
    SLSFS, 2005, pp:103-114 [Conf]
  21. Machiel Westerdijk, David Barber, Wim Wiegerinck
    Deterministic Generative Models for Fast Feature Discovery. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2001, v:5, n:4, pp:337-363 [Journal]
  22. Silvia Chiappa, David Barber
    EEG classification using generative independent component analysis. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2006, v:69, n:7-9, pp:769-777 [Journal]
  23. David Barber, Piërre van de Laar
    Variational Cumulant Expansions for Intractable Distributions. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 1999, v:10, n:, pp:435-455 [Journal]
  24. David Barber
    Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:2515-2540 [Journal]
  25. Jean-Pascal Pfister, Taro Toyoizumi, David Barber, Wulfram Gerstner
    Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2006, v:18, n:6, pp:1318-1348 [Journal]
  26. Peter Sollich, David Barber
    Online Learning from Finite Training Sets and Robustness to Input Bias. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1998, v:10, n:8, pp:2201-2217 [Journal]
  27. Christopher K. I. Williams, David Barber
    Bayesian Classification With Gaussian Processes. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Pattern Anal. Mach. Intell., 1998, v:20, n:12, pp:1342-1351 [Journal]
  28. David Barber, Silvia Chiappa
    Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:81-88 [Conf]
  29. David Barber, Bertrand Mesot
    A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:89-96 [Conf]

  30. Personalization of Cubic Hermite Meshes for Efficient Biomechanical Simulations. [Citation Graph (, )][DBLP]


  31. Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices. [Citation Graph (, )][DBLP]


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