Tom Heskes

Adriana Birlutiu, Perry Groot, Tom Heskes: Efficiently learning the preferences of people. Machine Learning 90(1): 1-28 (2013)

Max Hinne, Tom Heskes, Christian F. Beckmann, Marcel A. J. van Gerven: Bayesian inference of structural brain networks. NeuroImage 66: 543-552 (2013)

Evgeni Tsivtsivadze, Tom Heskes, Armand Paauw: Multi-view Multi-class Classification for Identification of Pathogenic Bacterial Strains. MCS 2013: 61-72

Tom Heskes, Onno Zoeter: Expectation Propogation for approximate inference in dynamic Bayesian networks. CoRR abs/1301.0572 (2013)

Wim Wiegerinck, Tom Heskes: IPF for Discrete Chain Factor Graphs. CoRR abs/1301.0613 (2013)

L. Niels Cornelisse, Evgeni Tsivtsivadze, Marieke Meijer, Tjeerd Dijkstra, Tom Heskes, Matthijs Verhage: Molecular Machines in the Synapse: Overlapping Protein Sets Control Distinct Steps in Neurosecretion. PLoS Computational Biology 8(4) (2012)

Daniel Kühlwein, Twan van Laarhoven, Evgeni Tsivtsivadze, Josef Urban, Tom Heskes: Overview and Evaluation of Premise Selection Techniques for Large Theory Mathematics. IJCAR 2012: 378-392

Tom de Ruijter, Evgeni Tsivtsivadze, Tom Heskes: Online Co-regularized Algorithms. Discovery Science 2012: 184-193

Tom Claassen, Tom Heskes: A Bayesian Approach to Constraint Based Causal Inference. UAI 2012: 207-216

Tom Claassen, Tom Heskes: A Logical Characterization of Constraint-Based Causal Discovery. CoRR abs/1202.3711 (2012)

Botond Cseke, Tom Heskes: Bounds on the Bethe Free Energy for Gaussian Networks. CoRR abs/1206.3243 (2012)

Tom Claassen, Tom Heskes: A Bayesian Approach to Constraint Based Causal Inference. CoRR abs/1210.4866 (2012)

Tom Heskes, Kees Albers, Hilbert J. Kappen: Approximate Inference and Constrained Optimization. CoRR abs/1212.2480 (2012)

Botond Cseke, Tom Heskes: Properties of Bethe Free Energies and Message Passing in Gaussian Models. J. Artif. Intell. Res. (JAIR) 41: 1-24 (2011)

Botond Cseke, Tom Heskes: Approximate Marginals in Latent Gaussian Models. Journal of Machine Learning Research 12: 417-454 (2011)

Marcel van Gerven, Peter Kok, Floris P. de Lange, Tom Heskes: Dynamic decoding of ongoing perception. NeuroImage 57(3): 950-957 (2011)

Perry Groot, Tom Heskes, Tjeerd Dijkstra, James M. Kates: Predicting Preference Judgments of Individual Normal and Hearing-Impaired Listeners With Gaussian Processes. IEEE Transactions on Audio, Speech & Language Processing 19(4): 811-821 (2011)

Marco Baglietto, Lubica Benusková, Ivo Bukovsky, Tianping Chen, Tom Heskes, Kazushi Ikeda, Fakhri Karray, Rhee Man Kil, Robert A. Legenstein, Jinhu Lu, Yunqian Ma, Malik Magdon-Ismail, Michael G. Paulin, Robi Polikar, Danil V. Prokhorov, Marco Wiering, Vicente Zarzoso: Editorial: One Year as EiC, and Editorial-Board Changes at TNN. IEEE Transactions on Neural Networks 22(1): 1-7 (2011)

Tom Claassen, Tom Heskes: A structure independent algorithm for causal discovery. ESANN 2011

John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl: Learning of causal relations. ESANN 2011

Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes: Multi-output Ranking for Automated Reasoning. KDIR 2011: 42-51

Marcel van Gerven, Eric Maris, Tom Heskes: A Markov Random Field Approach to Neural Encoding and Decoding. ICANN (2) 2011: 1-8

Perry Groot, Adriana Birlutiu, Tom Heskes: Learning from Multiple Annotators with Gaussian Processes. ICANN (2) 2011: 159-164

Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes: Learning2Reason. Calculemus/MKM 2011: 298-300

Ali Bahramisharif, Marcel A. J. van Gerven, Jan-Mathijs Schoffelen, Zoubin Ghahramani, Tom Heskes: The Dynamic Beamformer. MLINI 2011: 148-155

Hans J. P. Wouters, Marcel A. J. van Gerven, Matthias Sebastian Treder, Tom Heskes, Ali Bahramisharif: Covert Attention as a Paradigm for Subject-Independent Brain-Computer Interfacing. MLINI 2011: 156-163

Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf: On Causal Discovery with Cyclic Additive Noise Models. NIPS 2011: 639-647

Evgeni Tsivtsivadze, Josef Urban, Herman Geuvers, Tom Heskes: Semantic Graph Kernels for Automated Reasoning. SDM 2011: 795-803

Tom Claassen, Tom Heskes: A Logical Characterization of Constraint-Based Causal Discovery. UAI 2011: 135-144

Jesse Alama, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban, Tom Heskes: Premise Selection for Mathematics by Corpus Analysis and Kernel Methods. CoRR abs/1108.3446 (2011)

Adriana Birlutiu, Perry Groot, Tom Heskes: Multi-task preference learning with an application to hearing aid personalization. Neurocomputing 73(7-9): 1177-1185 (2010)

Botond Cseke, Tom Heskes: Improving posterior marginal approximations in latent Gaussian models. Journal of Machine Learning Research - Proceedings Track 9: 121-128 (2010)

Marcel van Gerven, Floris P. de Lange, Tom Heskes: Neural Decoding with Hierarchical Generative Models. Neural Computation 22(12): 3127-3142 (2010)

Marcel A. J. van Gerven, Botond Cseke, Floris P. de Lange, Tom Heskes: Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior. NeuroImage 50(1): 150-161 (2010)

Perry Groot, Adriana Birlutiu, Tom Heskes: Bayesian Monte Carlo for the Global Optimization of Expensive Functions. ECAI 2010: 249-254

Tom Claassen, Tom Heskes: Causal discovery in multiple models from different experiments. NIPS 2010: 415-423

Tom Heskes: Expectation Propagation. Encyclopedia of Machine Learning 2010: 383-387

Tjeerd Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori, Tom Heskes (Eds.): Pattern Recognition in Bioinformatics - 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings. Lecture Notes in Computer Science 6282, Springer 2010, isbn 978-3-642-16000-4

Rasa Jurgelenaite, Tjeerd Dijkstra, Clemens H. M. Kocken, Tom Heskes: Gene regulation in the intraerythrocytic cycle of Plasmodium falciparum. Bioinformatics 25(12): 1484-1491 (2009)

Marcel van Gerven, Christian Hesse, Ole Jensen, Tom Heskes: Interpreting single trial data using groupwise regularisation. NeuroImage 46(3): 665-676 (2009)

Marcel van Gerven, Ali Bahramisharif, Tom Heskes, Ole Jensen: Selecting features for BCI control based on a covert spatial attention paradigm. Neural Networks 22(9): 1271-1277 (2009)

Ali Bahramisharif, Marcel van Gerven, Tom Heskes: Exploring the impact of alternative feature representations on BCI classification. ESANN 2009

Adriana Birlutiu, Perry Groot, Tom Heskes: Multi-task Preference learning with Gaussian Processes. ESANN 2009

Marcel van Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes: Bayesian Source Localization with the Multivariate Laplace Prior. NIPS 2009: 1901-1909

Botond Cseke, Tom Heskes: Bounds on the Bethe Free Energy for Gaussian Networks. UAI 2008: 97-104

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)

Bart Bakker, Tom Heskes: Learning and approximate inference in dynamic hierarchical models. Computational Statistics & Data Analysis 52(2): 821-839 (2007)

José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. NIPS 2007

Adriana Birlutiu, Tom Heskes: Expectation Propagation for Rating Players in Sports Competitions. PKDD 2007: 374-381

Tom Heskes: Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies. J. Artif. Intell. Res. (JAIR) 26: 153-190 (2006)

Onno Zoeter, Tom Heskes: Deterministic approximate inference techniques for conditionally Gaussian state space models. Statistics and Computing 16(3): 279-292 (2006)

Rasa Jurgelenaite, Tom Heskes: EM Algorithm for Symmetric Causal Independence Models. ECML 2006: 234-245

Rasa Jurgelenaite, Tom Heskes: Symmetric Causal Independence Models for Classification. Probabilistic Graphical Models 2006: 163-170

Alexander Ypma, Tom Heskes: Novel approximations for inference in nonlinear dynamical systems using expectation propagation. Neurocomputing 69(1-3): 85-99 (2005)

Onno Zoeter, Tom Heskes: Change Point Problems in Linear Dynamical Systems. Journal of Machine Learning Research 6: 1999-2026 (2005)

Tom Heskes, Bert de Vries: Incremental Utility Elicitation for Adaptive Personalization. BNAIC 2005: 127-134

Rasa Jurgelenaite, Peter J. F. Lucas, Tom Heskes: Use of the Noisy Threshold Function in Building Bayesian Networks. BNAIC 2005: 158-165

Onno Zoeter, Tom Heskes: Gaussian Quadrature Based Expectation Propagation. BNAIC 2005: 407

Tom Heskes: On the Uniqueness of Loopy Belief Propagation Fixed Points. Neural Computation 16(11): 2379-2413 (2004)

Alexander Ypma, Tom Heskes: Novel approximations for inference and learning in nonlinear dynamical systems. ESANN 2004: 361-366

Bart Bakker, Tom Heskes: Task Clustering and Gating for Bayesian Multitask Learning. Journal of Machine Learning Research 4: 83-99 (2003)

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)

Bart Bakker, Tom Heskes: Clustering ensembles of neural network models. Neural Networks 16(2): 261-269 (2003)

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)

Onno Zoeter, Tom Heskes: Multi-scale Switching Linear Dynamical Systems. ICANN 2003: 562-572

Tom Heskes, Onno Zoeter, Wim Wiegerinck: Approximate Expectation Maximization. NIPS 2003

Tom Heskes, Kees Albers, Bert Kappen: Approximate Inference and Constrained Optimization. UAI 2003: 313-320

Tom Heskes, Bart Bakker, Bert Kappen: Approximate algorithms for neural-Bayesian approaches. Theor. Comput. Sci. 287(1): 219-238 (2002)

Bart Bakker, Tom Heskes: Model Clustering for Neural Network Ensembles. ICANN 2002: 383-388

Alexander Ypma, Tom Heskes: Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models. WEBKDD 2002: 35-49

Tom Heskes: Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. NIPS 2002: 343-350

Wim Wiegerinck, Tom Heskes: Fractional Belief Propagation. NIPS 2002: 438-445

Tom Heskes, Onno Zoeter: Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks. UAI 2002: 216-223

Wim Wiegerinck, Tom Heskes: IPF for Discrete Chain Factor Graphs. UAI 2002: 560-567

Tom Heskes: Self-organizing maps, vector quantization, and mixture modeling. IEEE Transactions on Neural Networks 12(6): 1299-1305 (2001)

Piërre van de Laar, Tom Heskes: Input selection based on an ensemble. Neurocomputing 34(1-4): 227-238 (2000)

Tom Heskes: On "Natural" Learning and Pruning in Multilayered Perceptrons. Neural Computation 12(4): 881-901 (2000)

Tom Heskes: Empirical Bayes for Learning to Learn. ICML 2000: 367-374

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

Tom Heskes, Jan-Joost Spanjers, Wim Wiegerinck: EM Algorithms for Self-Organizing Maps. IJCNN (6) 2000: 9-14

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)

Piërre van de Laar, Tom Heskes: Pruning Using Parameter and Neuronal Metrics. Neural Computation 11(4): 977-993 (1999)

Bart Bakker, Tom Heskes: Model clustering by deterministic annealing. ESANN 1999: 87-92

Tom Heskes: Bias/Variance Decompositions for Likelihood-Based Estimators. Neural Computation 10(6): 1425-1433 (1998)

Tom Heskes: Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach. ICML 1998: 233-241

Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen: Task-Dependent Learning of Attention. Neural Networks 10(6): 981-992 (1997)

Piërre van de Laar, Stan C. A. M. Gielen, Tom Heskes: Input Selection with Partial Retraining. ICANN 1997: 469-474

Tom Heskes: Selecting Weighting Factors in Logarithmic Opinion Pools. NIPS 1997

Tom Heskes: Transition times in self-organizing maps. Biological Cybernetics 75(1): 49-57 (1996)

Tom Heskes, Wim Wiegerinck: A theoretical comparison of batch-mode, on-line, cyclic, and almost-cyclic learning. IEEE Trans. Neural Netw. Learning Syst. 7(4): 919-925 (1996)

Tom Heskes: Practical Confidence and Prediction Intervals. NIPS 1996: 176-182

Tom Heskes: Balancing Between Bagging and Bumping. NIPS 1996: 466-472

Tom Heskes: Stochastics of on-line back-propagation. ESANN 1994

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)