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Tom Heskes: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Onno Zoeter, Tom Heskes
    Gaussian Quadrature Based Expectation Propagation. [Citation Graph (0, 0)][DBLP]
    BNAIC, 2005, pp:407- [Conf]
  2. Tom Heskes, Bert de Vries
    Incremental Utility Elicitation for Adaptive Personalization. [Citation Graph (0, 0)][DBLP]
    BNAIC, 2005, pp:127-134 [Conf]
  3. Rasa Jurgelenaite, Peter J. F. Lucas, Tom Heskes
    Use of the Noisy Threshold Function in Building Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    BNAIC, 2005, pp:158-165 [Conf]
  4. Rasa Jurgelenaite, Tom Heskes
    EM Algorithm for Symmetric Causal Independence Models. [Citation Graph (0, 0)][DBLP]
    ECML, 2006, pp:234-245 [Conf]
  5. Bart Bakker, Tom Heskes
    Model clustering by deterministic annealing. [Citation Graph (0, 0)][DBLP]
    ESANN, 1999, pp:87-92 [Conf]
  6. Alexander Ypma, Tom Heskes
    Novel approximations for inference and learning in nonlinear dynamical systems. [Citation Graph (0, 0)][DBLP]
    ESANN, 2004, pp:361-366 [Conf]
  7. Bart Bakker, Tom Heskes
    Model Clustering for Neural Network Ensembles. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:383-388 [Conf]
  8. Piërre van de Laar, Stan C. A. M. Gielen, Tom Heskes
    Input Selection with Partial Retraining. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:469-474 [Conf]
  9. Onno Zoeter, Tom Heskes
    Multi-scale Switching Linear Dynamical Systems. [Citation Graph (0, 0)][DBLP]
    ICANN, 2003, pp:562-572 [Conf]
  10. Tom Heskes
    Empirical Bayes for Learning to Learn. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:367-374 [Conf]
  11. Tom Heskes
    Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:233-241 [Conf]
  12. Jakob Vogdrup Hansen, Tom Heskes
    General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of Distributions. [Citation Graph (0, 0)][DBLP]
    ICPR, 2000, pp:2207-2210 [Conf]
  13. Tom Heskes, Jan-Joost Spanjers, Wim Wiegerinck
    EM Algorithms for Self-Organizing Maps. [Citation Graph (0, 0)][DBLP]
    IJCNN (6), 2000, pp:9-14 [Conf]
  14. Alexander Ypma, Tom Heskes
    Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models. [Citation Graph (0, 0)][DBLP]
    WEBKDD, 2002, pp:35-49 [Conf]
  15. Tom Heskes
    Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:343-350 [Conf]
  16. Tom Heskes
    Practical Confidence and Prediction Intervals. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:176-182 [Conf]
  17. Tom Heskes
    Balancing Between Bagging and Bumping. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:466-472 [Conf]
  18. Tom Heskes
    Selecting Weighting Factors in Logarithmic Opinion Pools. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  19. Tom Heskes, Onno Zoeter, Wim Wiegerinck
    Approximate Expectation Maximization. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  20. Wim Wiegerinck, Tom Heskes
    Fractional Belief Propagation. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:438-445 [Conf]
  21. Tom Heskes, Kees Albers, Bert Kappen
    Approximate Inference and Constrained Optimization. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:313-320 [Conf]
  22. Tom Heskes, Onno Zoeter
    Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    UAI, 2002, pp:216-223 [Conf]
  23. Wim Wiegerinck, Tom Heskes
    IPF for Discrete Chain Factor Graphs. [Citation Graph (0, 0)][DBLP]
    UAI, 2002, pp:560-567 [Conf]
  24. Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen
    Partial Retraining: A New Approach to Input Relevance Determination. [Citation Graph (0, 0)][DBLP]
    Int. J. Neural Syst., 1999, v:9, n:1, pp:75-85 [Journal]
  25. Piërre van de Laar, Tom Heskes
    Input selection based on an ensemble. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2000, v:34, n:1-4, pp:227-238 [Journal]
  26. Alexander Ypma, Tom Heskes
    Novel approximations for inference in nonlinear dynamical systems using expectation propagation. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:69, n:1-3, pp:85-99 [Journal]
  27. Bart Bakker, Tom Heskes
    Task Clustering and Gating for Bayesian Multitask Learning. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2003, v:4, n:, pp:83-99 [Journal]
  28. Onno Zoeter, Tom Heskes
    Change Point Problems in Linear Dynamical Systems. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2005, v:6, n:, pp:1999-2026 [Journal]
  29. Tom Heskes, Jan-Joost Spanjers, Bart Bakker, Wim Wiegerinck
    Optimising newspaper sales using neural-Bayesian technology. [Citation Graph (0, 0)][DBLP]
    Neural Computing and Applications, 2003, v:12, n:3-4, pp:212-219 [Journal]
  30. Tom Heskes
    On "Natural" Learning and Pruning in Multilayered Perceptrons. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:4, pp:881-901 [Journal]
  31. Tom Heskes
    On the Uniqueness of Loopy Belief Propagation Fixed Points. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2004, v:16, n:11, pp:2379-2413 [Journal]
  32. Tom Heskes
    Bias/Variance Decompositions for Likelihood-Based Estimators. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1998, v:10, n:6, pp:1425-1433 [Journal]
  33. Piërre van de Laar, Tom Heskes
    Pruning Using Parameter and Neuronal Metrics. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1999, v:11, n:4, pp:977-993 [Journal]
  34. Bart Bakker, Tom Heskes
    Clustering ensembles of neural network models. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2003, v:16, n:2, pp:261-269 [Journal]
  35. Tom Heskes, Stan C. A. M. Gielen
    Retrieval of pattern sequences at variable speeds in a neural network with delays. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1992, v:5, n:1, pp:145-152 [Journal]
  36. Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen
    Task-Dependent Learning of Attention. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1997, v:10, n:6, pp:981-992 [Journal]
  37. Onno Zoeter, Tom Heskes
    Hierarchical Visualization of Time-Series Data Using Switching Linear Dynamical Systems. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Pattern Anal. Mach. Intell., 2003, v:25, n:10, pp:1202-1214 [Journal]
  38. Tom Heskes, Bart Bakker, Bert Kappen
    Approximate algorithms for neural-Bayesian approaches. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 2002, v:287, n:1, pp:219-238 [Journal]
  39. Onno Zoeter, Tom Heskes
    Deterministic approximate inference techniques for conditionally Gaussian state space models. [Citation Graph (0, 0)][DBLP]
    Statistics and Computing, 2006, v:16, n:3, pp:279-292 [Journal]
  40. Adriana Birlutiu, Tom Heskes
    Expectation Propagation for Rating Players in Sports Competitions. [Citation Graph (0, 0)][DBLP]
    PKDD, 2007, pp:374-381 [Conf]
  41. Tom Heskes
    Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2006, v:26, n:, pp:153-190 [Journal]

  42. Bayesian Monte Carlo for the Global Optimization of Expensive Functions. [Citation Graph (, )][DBLP]


  43. Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. [Citation Graph (, )][DBLP]


  44. Symmetric Causal Independence Models for Classification. [Citation Graph (, )][DBLP]


  45. Bounds on the Bethe Free Energy for Gaussian Networks. [Citation Graph (, )][DBLP]


  46. Predicting carcinoid heart disease with the noisy-threshold classifier. [Citation Graph (, )][DBLP]


  47. Transition times in self-organizing maps. [Citation Graph (, )][DBLP]


  48. Gene regulation in the intraerythrocytic cycle of Plasmodium falciparum. [Citation Graph (, )][DBLP]


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