Evgeni Tsivtsivadze,
Tapio Pahikkala,
Jorma Boberg,
Tapio Salakoski,
and Tom Heskes.
Co-regularized least-squares for label ranking.
In Johannes Fürnkranz and Eyke Hüllermeier, editors, Preference Learning,
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Springer,
2011.
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Expectation propagation and generalised EP methods for inference in switching Kalman filter models.
In David Barber,
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Cambridge University Press,
2011.
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Lateralized responses during covert attention are modulated by target eccentricity.
Neuroscience Letters,
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2011.
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Botond Cseke and Tom Heskes.
Approximate marginals in latent Gaussian models.
Journal of Machine Learning Research,
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2011.
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Botond Cseke and Tom Heskes.
Properties of Bethe free energies and message passing in Gaussian models.
Journal of Artificial Intelligence Research,
41:1-24,
2011.
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Perry Groot,
Tom Heskes,
Tjeerd Dijkstra,
and James Kates.
Predicting preference judgments of individual normal and hearing-impaired listeners with Gaussian processes.
IEEE Transactions on Audio, Sound, and Language Processing,
19:811-821,
2011.
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Marcel van Gerven,
Peter Kok,
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Dynamic decoding of ongoing perception.
Neuroimage,
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2011.
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Expectation propagation.
In Claude Sammut and Geoffrey Webb, editors, Encyclopedia of Machine Learning.
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Covert attention allows for continuous control of brain-computer interfaces.
European Journal of Neuroscience,
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2010.
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Perry Groot,
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Multi-task preference learning with an application to hearing aid personalization.
Neurocomputing,
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2010.
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Marcel van Gerven,
Botond Cseke,
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Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior.
Neuroimage,
50:150-161,
2010.
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Marcel van Gerven,
Floris de Lange,
and Tom Heskes.
Neural decoding with hierarchical generative models.
Neural Computation,
22:3127-3142,
2010.
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Tom Heskes and Botond Cseke.
Discussion on `Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations' by H. Rue, S. Martino, and N. Chopin.
Journal of the Royal Statistical Society Series B.,
71:370,
2009.
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Rasa Jurgelenaite,
Tjeerd Dijkstra,
Clemens Kocken,
and Tom Heskes.
Gene regulation in the intraerythrocytic cycle of Plasmodium Falciparum.
Bioinformatics,
25:1484-1491,
2009.
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Marcel van Gerven,
Ali Bahramisharif,
Tom Heskes,
and Ole Jensen.
Selecting features for BCI control based on a covert spatial attention paradigm.
Neural Networks,
22:1271-1277,
2009.
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Christian Hesse,
Ole Jensen,
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Interpreting single trial data using groupwise regularisation.
Neuroimage,
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2009.
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Rasa Jurgelenaite and Tom Heskes.
Learning symmetric causal independence models.
Machine Learning,
71:133-153,
2008.
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Kees Albers,
Tom Heskes,
and Bert Kappen.
Haplotype inference in general pedigrees using the cluster variation method.
Genetics,
177:1101-1116,
2007.
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Bart Bakker and Tom Heskes.
Learning and approximate inference in dynamic hierarchical models.
Computational Statistics & Data Analysis,
52:821-839,
2007.
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Marcel van Gerven,
Rasa Jurgelenaite,
Babs Taal,
Tom Heskes,
and Peter Lucas.
Predicting carcinoid heart disease with the noisy-threshold classifier.
Artificial Intelligence in Medicine,
40:45-55,
2007.
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Convexity arguments for efficient minimization of the Bethe and Kikuchi free energies.
Journal of Artificial Intelligence Research,
26:153-190,
2006.
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Onno Zoeter and Tom Heskes.
Deterministic approximate inference techniques for conditionally Gaussian state space models.
Statistics and Computing,
16:279-292,
2006.
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Manfred Opper,
Wim Wiegerinck,
Ole Winther,
and Onno Zoeter.
Approximate inference techniques with expectation constraints.
Journal of Statistical Mechanics: Theory and Experiment,
2005:P11015,
2005.
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Novel approximations for inference in nonlinear dynamical systems using expectation propagation.
Neurocomputing,
69:85-99,
2005.
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Onno Zoeter and Tom Heskes.
Change point problems in linear dynamical systems.
Journal of Machine Learning Research,
6:1999-2026,
2005.
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David Barber and Tom Heskes.
An introduction to neural networks.
In Encyclopedia of Life Support Systems.
2004.
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Tom Heskes,
Jan Neijt,
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Improving Cox survival analysis with a neural-Bayesian approach.
Statistics in Medicine,
23:2989-3012,
2004.
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Tom Heskes.
On the uniqueness of loopy belief propagation fixed points.
Neural Computation,
16:2379-2413,
2004.
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Jan-Joost Spanjers and Tom Heskes.
Neural networks for modeling volatility and market capitalization.
In Gordian Gaeta,
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2003.
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Task clustering and gating for Bayesian multitask learning.
Journal of Machine Learning Research,
4:83-99,
2003.
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Jan-Joost Spanjers,
Bart Bakker,
and Wim Wiegerinck.
Optimising newspaper sales using neural-Bayesian technology.
Neural Computing and Applications,
12:212-219,
2003.
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Hierarchical visualization of time-series data using switching linear dynamical systems.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
25:1201-1214,
2003.
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Wim Wiegerinck and Tom Heskes.
Belief networks/Bayesian networks.
In J. Meij, editor, Dealing with the data flow. Mining data, text and multimedia,
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The Hague, The Netherlands,
2002.
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Clustering ensembles of neural network models.
Neural Networks,
16:261-269,
2002.
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Bart Bakker,
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Approximate Algorithms for Neural-Bayesian Approaches.
Theoretical Computer Science,
287:219-238,
2002.
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The use of being stubborn and introspective.
In H. Ritter,
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Kluwer,
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2001.
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Self-organizing maps, vector quantization, and mixture modeling.
IEEE Transactions on Neural Networks,
12:1299-1305,
2001.
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Tom Heskes.
On natural learning and pruning in multilayered perceptrons.
Neural Computation,
12:1037-1057,
2000.
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Gabe Sonke,
Tom Heskes,
André Verbeek,
Jean De La Rosette,
and Bart Kiemeney.
Prediction of bladder outlet obstruction in men with lower urinary tract symptoms using artificial neural networks.
Journal of Urology,
163:300-305,
2000.
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Piërre van de Laar and Tom Heskes.
Input selection based on an ensemble.
Neurocomputing,
34:227-238,
2000.
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Tom Heskes.
Energy functions for self-organizing maps.
In E. Oja and S. Kaski, editors, Kohonen Maps,
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Elsevier,
Amsterdam,
1999.
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Piërre van de Laar and Tom Heskes.
Pruning using parameter and neuronal metrics.
Neural Computation,
11:977-993,
1999.
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Tom Heskes,
and Stan Gielen.
Partial retraining: a new approach to input relevance determination.
International Journal of Neural Systems,
9:75-85,
1999.
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On-line learning with time-correlated examples.
In D. Saad, editor, On-line Learning in Neural Networks,
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Tom Heskes.
Bias/variance decompositions for likelihood-based estimators.
Neural Computation,
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1998.
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Tom Heskes and Jeroen Coolen.
Learning in two-layered networks with correlated examples.
Journal of Physics A,
30:4983-4992,
1997.
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Piërre van de Laar,
Tom Heskes,
and Stan Gielen.
Task-dependent learning of attention.
Neural Networks,
10:981-992,
1997.
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Tom Heskes.
Transition times in self-organizing maps.
Biological Cybernetics,
75:49-57,
1996.
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Tom Heskes and Wim Wiegerinck.
A theoretical comparison of batch-mode, on-line, cyclic, and almost cyclic learning.
IEEE Transactions on Neural Networks,
7:919-925,
1996.
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Wim Wiegerinck and Tom Heskes.
How dependencies between successive examples affect on-line learning.
Neural Computation,
8:1743-1765,
1996.
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Tom Heskes.
On Fokker-Planck approximations of on-line learning processes.
Journal of Physics A,
27:5145-5160,
1994.
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Wim Wiegerinck and Tom Heskes.
On-line learning with time-correlated patterns.
Europhysics Letters,
28:451-455,
1994.
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Wim Wiegerinck,
Andrzej Komoda,
and Tom Heskes.
Stochastic dynamics of learning with momentum in neural networks.
Journal of Physics A,
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On-line learning processes in artificial neural networks.
In J. Taylor, editor, Mathematical Approaches to Neural Networks,
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Eddy Slijpen,
and Bert Kappen.
Cooling schedules for learning in neural networks.
Physical Review E,
47:4457-4464,
1993.
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Tom Heskes and Stan Gielen.
Retrieval of pattern sequences at variable speeds in a neural network with delays.
Neural Networks,
5:145-152,
1992.
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Tom Heskes and Bert Kappen.
Learning-parameter adjustment in neural networks.
Physical Review A,
45:8885-8893,
1992.
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Tom Heskes,
Eddy Slijpen,
and Bert Kappen.
Learning in neural networks with local minima.
Physical Review A,
46:5221-5231,
1992.
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Tom Heskes and Bert Kappen.
Learning processes in neural networks.
Physical Review A,
44:2718-2726,
1991.
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Ali Bahramisharif,
Marcel van Gerven,
Jan-Mathijs Schoffelen,
Zoubin Ghahramani,
and Tom Heskes.
The dynamic beamformer.
In NIPS workshop on Machine Learning and Interpretation in Neuroimaging,
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A logical characterization of constraint-based causal discovery.
In UAI 2011, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence,
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A structure independent algorithm for causal discovery.
In ESANN 2011,
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Learning from multiple annotators with Gaussian processes.
In Lecture Notes in Computer Science, Volume 6792, Artificial Neural Networks and Machine Learning – ICANN 2011,
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On Causal Discovery with Cyclic Additive Noise Models.
In Advances in Neural Information Processing Systems 24 (NIPS*2011),
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Learning of causal relations.
In ESANN 2011,
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Josef Urban,
Herman Geuvers,
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Semantic graph kernels for automated reasoning.
In Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011,
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2011.
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Covert attention as a paradigm for subject-independent brain-computer interfacing.
In NIPS workshop on Machine Learning and Interpretation in Neuroimaging,
2011.
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Marcel van Gerven,
Eric Maris,
and Tom Heskes.
Markov random field approach to neural encoding and decoding.
In Lecture Notes in Computer Science, Volume 6792, Artificial Neural Networks and Machine Learning – ICANN 2011,
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2011.
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Causal discovery in multiple models from different experiments.
In John Lafferty,
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2010.
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Learning causal network structure from multiple (in)dependence models.
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Improving posterior marginal approximations in latent Gaussian models.
In Yee Whye Teh and Mike Titterington, editors,
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2010.
JMLR Workshop and Conference Proceedings.
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Adriana Birlutiu,
and Tom Heskes.
Bayesian Monte Carlo for the global optimization of expensive functions.
In Helder Coelho,
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pages 249-254,
2010.
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Sparse preference learning.
In Irina Rish,
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NIPS Workshop on Practical Application of Sparse Modeling: Open Issues and New Directions,
2010.
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Adriana Birlutiu,
Celine Brouard,
Tom Heskes,
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Regularized output kernel regression for protein-protein interaction prediction: application to link transfer and transduction.
In Machine Learning in Computational Biology,
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Sparse orthonormalized partial least squares.
In BNAIC 2010,
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Floris de Lange,
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A hierarchical generative model for percept reconstruction.
In Human Brain Mapping,
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Marcel van Gerven,
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Exploring the impact of alternative feature representations on BCI classification.
In Proceedings of ESANN'2009,
pages 455-460,
2009.
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Perry Groot,
and Tom Heskes.
Multi-task preference learning with Gaussian Processes.
In Proceedings of ESANN'2009,
pages 123-128,
2009.
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Niels Cornelisse,
Evgeni Tsivtsivadze,
Marieke Meijer,
Tjeerd Dijkstra,
Tom Heskes,
and Mathijs Verhage.
Identification of presynaptic gene clusters in synaptic signaling using functional data from genetic perturbation studies in Hippocampal autapses.
In 2nd INCF Congress of Neuroinformatics, Frontiers in Neuroinformatics (abstract),
2009.
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Botond Cseke,
and Tom Heskes.
Kernel principal component ranking: robust ranking on noisy data.
In Eyke Hüllermeier and Johannes Fürnkranz, editors,
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pages 101-113,
2009.
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Marcel van Gerven,
Botond Cseke,
Robert Oostenveld,
and Tom Heskes.
Bayesian source localization with the multivariate Laplace prior.
In Y. Bengio,
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Bounds on the Bethe free energy for Gaussian networks.
In David A. McAllester and Petri Myllymäki, editors,
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pages 97-104,
2008.
AUAI Press.
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Tjeerd Dijkstra,
and Tom Heskes.
Regulator discovery from gene expression time series of malaria parasites: a hierarchical approach.
In J.C. Platt,
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2008.
MIT Press.
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Christian Hesse,
Tom Heskes,
and Ole Jensen.
Semi-blind identification of movement-related magnetoencephalogram components using a classification approach.
In Engineering in Medicine and Biology Society, 2008. 30th Annual International Conference of the IEEE,
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2008.
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Expectation propagation for rating players in sports competitions.
In Joost N. Kok,
Jacek Koronacki,
Ramon López de Mántaras,
Stan Matwin,
Dunja Mladenic,
and Andrzej Skowron, editors,
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volume 4702 of Lecture Notes in Computer Science,
pages 374-381,
2007.
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Robert Oostenveld,
Tom Heskes,
and Ole Jensen.
On the development of a brain-computer interface system using high-density magnetoencephalogram signals for real-time control of a robot arm.
In Annual Symposium of the IEEE-EMBS Benelux Chapter,
2007.
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Tom Heskes,
and Tjeerd Dijkstra.
Using symmetric causal independence models to predict gene expression from sequence data.
In A. Fazel Famili,
Xiaohui Liu,
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Proceedings of the 2nd Workshop in Data Mining in Functional Genomics and Proteomics,
pages 67-78,
2007.
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Christian Hesse,
Daan Holtackers,
and Tom Heskes.
On the use of mixtures of Gaussians and mixtures of generalized exponentials for modelling and classifying biomedical signals.
In Proceedings of the 1st Annual Symposium IEEE EMBS Benelux Chapter,
Brussels, Belgium,
2006.
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EM Algorithm for Symmetric Causal Independence Models.
In Johannes Fürnkranz,
Tobias Scheffer,
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volume 4212 of Lecture Notes in Computer Science,
pages 234-245,
2006.
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Symmetric causal independence models for classification.
In Proceedings of the Third European Workshop on Probabilistic Graphical Models,
pages 163-170,
2006.
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Alexander Ypma,
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Deterministic and stochastic Gaussian particle smoothing.
In Proceedings of the 2006 Nonlinear Statistical Signal Processing Workshop (Cambridge, UK, September 13-15, 2006),
Piscataway, NJ,
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Incremental utility elicitation for adaptive personalization.
In K. Verbeeck,
K. Tuyls,
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B. Manderick,
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BNAIC 2005, Proceedings of the Seventeenth Belgium-Netherlands Conference on Artificial Intelligence,
Brussels,
pages 127-134,
2005.
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Peter Lucas,
and Tom Heskes.
Exploring the noisy threshold function in designing Bayesian networks.
In Max Bramer,
Frans Coenen,
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Proceedings of AI-2005, the Twenty-fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence,
pages 133-146,
2005.
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Peter Lucas,
and Tom Heskes.
Use of the noisy threshold function in building Bayesian networks.
In K. Verbeeck,
K. Tuyls,
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B. Manderick,
and B. Kuijpers, editors,
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Brussels,
pages 158-165,
2005.
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Gaussian quadrature based expectation propagation.
In Z. Ghahramani and R. Cowell, editors,
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pages 445-452,
2005.
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Tom Heskes,
Onno Zoeter,
and Wim Wiegerinck.
Approximate Expectation Maximization.
In S. Thrun,
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2004.
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Alexander Ypma and Tom Heskes.
Novel approximations for inference and learning in nonlinear dynamical systems.
In 12th European Symposium on Artificial Neural Networks ESANN'04,
Brugge, Belgium,
pages 361-366,
2004.
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Alexander Ypma,
Machiel Westerdijk,
Henk-Jaap de Walle,
and Tom Heskes.
Bayesian techniques for modelling dynamic patterns.
In 4th European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems EUNITE 2004,
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2004.
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Onno Zoeter,
Alexander Ypma,
and Tom Heskes.
Improved unscented Kalman smoothing for stock volatility estimation.
In A. Bassos,
J. Principe,
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T. Adali,
and S. Douglas, editors,
Proceedings of the 2004 IEEE International Workshop on Machine Learning for Signal Processing,
São Luis, Brazil,
pages 143-152,
2004.
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Stable fixed points of loopy belief propagation are minima of the Bethe free energy.
In S. Becker,
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Kees Albers,
and Bert Kappen.
Approximate inference and constrained optimization.
In U. Kjærulff and C. Meek, editors,
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Generalized belief propagation for approximate inference in hybrid Bayesian networks.
In C. Bishop and B. Frey, editors,
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Note: Section 4.2 with results on discrete children with continuous children has been adapted after bug removal yielded better performance and correspondence with previous work; many apologies for any confusion.
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Fractional Belief Propagation.
In S. Thrun S. Becker and K. Obermayer, editors,
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pages 438-445,
2003.
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Automatic categorization of web pages and user clustering with mixtures of hidden Markov models.
In Osmar Zaïane,
Jaideep Srivastava,
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pages 35-49,
2003.
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Iterated extended Kalman smoothing with Expectation-Propagation.
In IEEE International Workshop on Neural Networks for Signal Processing NNSP 2003,
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pages 219-228,
2003.
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Multi-scale Switching Linear Dynamical Systems.
In Okyay Kaynak,
Ethem Alpaydin,
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volume 2714 of Lecture Notes in Computer Science,
pages 562–569,
2003.
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Model Clustering for Neural Network Ensembles.
In ICANN '02: Proceedings of the International Conference on Artificial Neural Networks,
volume 2415 of Lecture Notes in Computer Science,
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Expectation propagation for approximate inference in dynamic Bayesian networks.
In A. Darwiche and N. Friedman, editors,
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Note: A more detailed technical report can be found here.
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Visualization of process data with dynamic Bayesian networks.
In Proceedings of EUNITE 2002,
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Wim Wiegerinck and Tom Heskes.
IPF for discrete chain factor graphs.
In Proceedings of the 18th Annual Conference on Uncertainty in Artificial Intelligence (UAI-02),
San Francisco, CA,
pages 560-56,
2002.
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Task clustering for learning to learn.
In B. Kröse,
M. de Rijke,
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BNAIC'01: Proceedings of the 13th Belgium-Netherlands Artificial Intelligence Conference,
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2001.
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Wim Wiegerinck and Tom Heskes.
Probability assessment with maximum entropy in Bayesian networks.
In A. Goodman and P. Smyth, editors,
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2001.
Note: Also presented at AIME'01, Workshop Bayesian Models In Medicine, pages 71-80.
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Bart Bakker,
Bert Kappen,
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Survival analysis: a neural-Bayesian approach.
In H. Malmgren,
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Jakob-Vogdrup Hansen and Tom Heskes.
General bias/variance decomposition with target independent variance of error functions derived from the exponential family of distributions.
In A. Sanfeliu,
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Empirical Bayes for learning to learn.
In P. Langley, editor,
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2000.
Morgan Kaufmann.
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EM algorithms for self-organizing maps.
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2000.
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Wim Wiegerinck,
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Martijn Leisink,
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Sybert Stroeve,
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Variational methods for approximate reasoning in graphical models.
In Proceedings of RWC'2000,
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2000.
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Arthur Mynett,
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Resampling techniques for the assessment of uncertainties in parameters and predictions of calibrated models.
In Proceedings of Hydroinformatics 2000,
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2000.
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Bart Bakker and Tom Heskes.
A neural-Bayesian approach to survival analysis.
In Proceedings of ICANN99,
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1999.
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Bart Bakker and Tom Heskes.
Model clustering by deterministic annealing.
In M. Verleysen, editor,
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pages 87-92,
1999.
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Tom Heskes.
Selecting weighting factors in logarithmic opinion pools.
In M. Jordan,
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1998.
MIT Press.
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Tom Heskes.
Solving a huge number of similar tasks: a combination of multi-task learning and a hierarchical Bayesian approach.
In Proceedings of the International Conference on Machine Learning,
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1998.
Morgan Kaufmann.
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David Barber,
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Probabilistic knowledge representation.
In Proceedings of RWC’98,
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1998.
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Tom Heskes.
Balancing between bagging and bumping.
In M. Mozer,
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1997.
MIT Press.
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Tom Heskes.
Practical confidence and prediction intervals.
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1997.
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Practical confidence and prediction intervals for prediction tasks.
In Bert Kappen and Stan Gielen, editors,
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World Scientific, Singapore.
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Piërre van de Laar,
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Input selection with partial retraining.
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Berlin,
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1997.
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Tom Heskes and Bert Kappen.
Self-organization and nonparametric regression.
In F. Fogelman-Soulié and P. Gallinari, editors,
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Paris, France,
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1995.
EC2 & Cie.
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Bert Kappen,
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Confidence values for neural networks.
In Proceedings of the International Conference on Digital Signal Processing,
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1995.
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Tom Heskes and Wim Wiegerinck.
Presentation order and on-line learning.
In F. Fogelman-Soulié and P. Gallinari, editors,
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Paris, France,
pages 223-228,
1995.
EC2 & Cie.
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André Pastoors and Tom Heskes.
Output coding and modularity for multi-class problems.
In Bert Kappen and Stan Gielen, editors,
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1995.
Springer-Verlag.
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Piërre van de Laar,
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A neural model of visual attention.
In Bert Kappen and Stan Gielen, editors,
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Tom Heskes.
Stochastics of on-line backpropagation.
In Proceedings of the European Symposium on Artificial Neural Networks '94,
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1994.
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Tom Heskes,
Wim Wiegerinck,
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Scaling properties of on-line learning with momentum.
In Proceedings of the IEEE-IJCNN '94,
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1994.
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Wim Wiegerinck,
Andrzej Komoda,
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On-line learning with momentum for nonlinear learning rules.
In M. Marinaro and G. Morasso, editors,
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London,
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1994.
Springer-Verlag.
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Guaranteed convergence of learning rules.
In H. Kappen and C. Gielen, editors,
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1993.
Springer-Verlag.
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Tom Heskes and Bert Kappen.
Error potentials for self-organization.
In International Conference on Neural Networks, San Francisco,
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1993.
IEEE.
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Tom Heskes and Eddy Slijpen.
Global performance of learning rules.
In I. Aleksander and J. Taylor, editors,
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1992.
North-Holland.
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Bert Kappen and Tom Heskes.
Learning rules, stochastic processes, and local minima.
In I. Aleksander and J. Taylor, editors,
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1992.
North-Holland.
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Tom Heskes,
Bert Kappen,
and Stan Gielen.
Neural networks learning in a changing environment.
In T. Kohonen,
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pages 15-20,
1991.
North-Holland.
Note: Also presented at IJCNN'91, volume 1, pages 823-828.
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