2008 |
46 | EE | Botond Cseke,
Tom Heskes:
Bounds on the Bethe Free Energy for Gaussian Networks.
UAI 2008: 97-104 |
2007 |
45 | EE | José Miguel Hernández-Lobato,
Tjeerd Dijkstra,
Tom Heskes:
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach.
NIPS 2007 |
44 | EE | Adriana Birlutiu,
Tom Heskes:
Expectation Propagation for Rating Players in Sports Competitions.
PKDD 2007: 374-381 |
43 | EE | Marcel van Gerven,
Rasa Jurgelenaite,
Babs G. Taal,
Tom Heskes,
Peter J. F. Lucas:
Predicting carcinoid heart disease with the noisy-threshold classifier.
Artificial Intelligence in Medicine 40(1): 45-55 (2007) |
42 | EE | Bart Bakker,
Tom Heskes:
Learning and approximate inference in dynamic hierarchical models.
Computational Statistics & Data Analysis 52(2): 821-839 (2007) |
2006 |
41 | EE | Rasa Jurgelenaite,
Tom Heskes:
EM Algorithm for Symmetric Causal Independence Models.
ECML 2006: 234-245 |
40 | EE | Rasa Jurgelenaite,
Tom Heskes:
Symmetric Causal Independence Models for Classification.
Probabilistic Graphical Models 2006: 163-170 |
39 | EE | Tom Heskes:
Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies.
J. Artif. Intell. Res. (JAIR) 26: 153-190 (2006) |
38 | EE | Onno Zoeter,
Tom Heskes:
Deterministic approximate inference techniques for conditionally Gaussian state space models.
Statistics and Computing 16(3): 279-292 (2006) |
2005 |
37 | | Tom Heskes,
Bert de Vries:
Incremental Utility Elicitation for Adaptive Personalization.
BNAIC 2005: 127-134 |
36 | | Rasa Jurgelenaite,
Peter J. F. Lucas,
Tom Heskes:
Use of the Noisy Threshold Function in Building Bayesian Networks.
BNAIC 2005: 158-165 |
35 | | Onno Zoeter,
Tom Heskes:
Gaussian Quadrature Based Expectation Propagation.
BNAIC 2005: 407 |
34 | EE | Onno Zoeter,
Tom Heskes:
Change Point Problems in Linear Dynamical Systems.
Journal of Machine Learning Research 6: 1999-2026 (2005) |
33 | EE | Alexander Ypma,
Tom Heskes:
Novel approximations for inference in nonlinear dynamical systems using expectation propagation.
Neurocomputing 69(1-3): 85-99 (2005) |
2004 |
32 | EE | Alexander Ypma,
Tom Heskes:
Novel approximations for inference and learning in nonlinear dynamical systems.
ESANN 2004: 361-366 |
31 | EE | Tom Heskes:
On the Uniqueness of Loopy Belief Propagation Fixed Points.
Neural Computation 16(11): 2379-2413 (2004) |
2003 |
30 | EE | Onno Zoeter,
Tom Heskes:
Multi-scale Switching Linear Dynamical Systems.
ICANN 2003: 562-572 |
29 | EE | Tom Heskes,
Onno Zoeter,
Wim Wiegerinck:
Approximate Expectation Maximization.
NIPS 2003 |
28 | | Tom Heskes,
Kees Albers,
Bert Kappen:
Approximate Inference and Constrained Optimization.
UAI 2003: 313-320 |
27 | EE | Onno Zoeter,
Tom Heskes:
Hierarchical Visualization of Time-Series Data Using Switching Linear Dynamical Systems.
IEEE Trans. Pattern Anal. Mach. Intell. 25(10): 1202-1214 (2003) |
26 | EE | Bart Bakker,
Tom Heskes:
Task Clustering and Gating for Bayesian Multitask Learning.
Journal of Machine Learning Research 4: 83-99 (2003) |
25 | EE | Tom Heskes,
Jan-Joost Spanjers,
Bart Bakker,
Wim Wiegerinck:
Optimising newspaper sales using neural-Bayesian technology.
Neural Computing and Applications 12(3-4): 212-219 (2003) |
24 | EE | Bart Bakker,
Tom Heskes:
Clustering ensembles of neural network models.
Neural Networks 16(2): 261-269 (2003) |
2002 |
23 | EE | Bart Bakker,
Tom Heskes:
Model Clustering for Neural Network Ensembles.
ICANN 2002: 383-388 |
22 | EE | Tom Heskes:
Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy.
NIPS 2002: 343-350 |
21 | EE | Wim Wiegerinck,
Tom Heskes:
Fractional Belief Propagation.
NIPS 2002: 438-445 |
20 | | Tom Heskes,
Onno Zoeter:
Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks.
UAI 2002: 216-223 |
19 | | Wim Wiegerinck,
Tom Heskes:
IPF for Discrete Chain Factor Graphs.
UAI 2002: 560-567 |
18 | EE | Alexander Ypma,
Tom Heskes:
Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models.
WEBKDD 2002: 35-49 |
17 | | Tom Heskes,
Bart Bakker,
Bert Kappen:
Approximate algorithms for neural-Bayesian approaches.
Theor. Comput. Sci. 287(1): 219-238 (2002) |
2000 |
16 | | Tom Heskes:
Empirical Bayes for Learning to Learn.
ICML 2000: 367-374 |
15 | EE | Jakob Vogdrup Hansen,
Tom Heskes:
General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of Distributions.
ICPR 2000: 2207-2210 |
14 | EE | Tom Heskes,
Jan-Joost Spanjers,
Wim Wiegerinck:
EM Algorithms for Self-Organizing Maps.
IJCNN (6) 2000: 9-14 |
13 | | Tom Heskes:
On "Natural" Learning and Pruning in Multilayered Perceptrons.
Neural Computation 12(4): 881-901 (2000) |
12 | EE | Piërre van de Laar,
Tom Heskes:
Input selection based on an ensemble.
Neurocomputing 34(1-4): 227-238 (2000) |
1999 |
11 | EE | Bart Bakker,
Tom Heskes:
Model clustering by deterministic annealing.
ESANN 1999: 87-92 |
10 | EE | Piërre van de Laar,
Tom Heskes,
Stan C. A. M. Gielen:
Partial Retraining: A New Approach to Input Relevance Determination.
Int. J. Neural Syst. 9(1): 75-85 (1999) |
9 | | Piërre van de Laar,
Tom Heskes:
Pruning Using Parameter and Neuronal Metrics.
Neural Computation 11(4): 977-993 (1999) |
1998 |
8 | | Tom Heskes:
Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach.
ICML 1998: 233-241 |
7 | | Tom Heskes:
Bias/Variance Decompositions for Likelihood-Based Estimators.
Neural Computation 10(6): 1425-1433 (1998) |
1997 |
6 | | Piërre van de Laar,
Stan C. A. M. Gielen,
Tom Heskes:
Input Selection with Partial Retraining.
ICANN 1997: 469-474 |
5 | | Tom Heskes:
Selecting Weighting Factors in Logarithmic Opinion Pools.
NIPS 1997 |
4 | EE | Piërre van de Laar,
Tom Heskes,
Stan C. A. M. Gielen:
Task-Dependent Learning of Attention.
Neural Networks 10(6): 981-992 (1997) |
1996 |
3 | EE | Tom Heskes:
Practical Confidence and Prediction Intervals.
NIPS 1996: 176-182 |
2 | EE | Tom Heskes:
Balancing Between Bagging and Bumping.
NIPS 1996: 466-472 |
1992 |
1 | EE | Tom Heskes,
Stan C. A. M. Gielen:
Retrieval of pattern sequences at variable speeds in a neural network with delays.
Neural Networks 5(1): 145-152 (1992) |