Please note: This is a beta version of the new dblp website.
You can find the classic dblp view of this page here.
You can find the classic dblp view of this page here.
Tom Heskes
2010 – today
- 2013
[j37]Adriana Birlutiu, Perry Groot, Tom Heskes: Efficiently learning the preferences of people. Machine Learning 90(1): 1-28 (2013)- [j36]Max Hinne, Tom Heskes, Christian F. Beckmann, Marcel A. J. van Gerven: Bayesian inference of structural brain networks. NeuroImage 66: 543-552 (2013)
- [c48]Evgeni Tsivtsivadze, Tom Heskes, Armand Paauw: Multi-view Multi-class Classification for Identification of Pathogenic Bacterial Strains. MCS 2013: 61-72
- [i7]Tom Heskes, Onno Zoeter: Expectation Propogation for approximate inference in dynamic Bayesian networks. CoRR abs/1301.0572 (2013)
- [i6]
- 2012
- [j35]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)
- [c47]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
- [c46]Tom de Ruijter, Evgeni Tsivtsivadze, Tom Heskes: Online Co-regularized Algorithms. Discovery Science 2012: 184-193
- [c45]Tom Claassen, Tom Heskes: A Bayesian Approach to Constraint Based Causal Inference. UAI 2012: 207-216
- [i5]Tom Claassen, Tom Heskes: A Logical Characterization of Constraint-Based Causal Discovery. CoRR abs/1202.3711 (2012)
- [i4]Botond Cseke, Tom Heskes: Bounds on the Bethe Free Energy for Gaussian Networks. CoRR abs/1206.3243 (2012)
- [i3]Tom Claassen, Tom Heskes: A Bayesian Approach to Constraint Based Causal Inference. CoRR abs/1210.4866 (2012)
- [i2]Tom Heskes, Kees Albers, Hilbert J. Kappen: Approximate Inference and Constrained Optimization. CoRR abs/1212.2480 (2012)
- 2011
- [j34]Botond Cseke, Tom Heskes: Properties of Bethe Free Energies and Message Passing in Gaussian Models. J. Artif. Intell. Res. (JAIR) 41: 1-24 (2011)
- [j33]Botond Cseke, Tom Heskes: Approximate Marginals in Latent Gaussian Models. Journal of Machine Learning Research 12: 417-454 (2011)
- [j32]Marcel van Gerven, Peter Kok, Floris P. de Lange, Tom Heskes: Dynamic decoding of ongoing perception. NeuroImage 57(3): 950-957 (2011)
- [j31]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)
- [j30]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)
- [c44]
- [c43]
- [c42]Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes: Multi-output Ranking for Automated Reasoning. KDIR 2011: 42-51
- [c41]Marcel van Gerven, Eric Maris, Tom Heskes: A Markov Random Field Approach to Neural Encoding and Decoding. ICANN (2) 2011: 1-8
- [c40]Perry Groot, Adriana Birlutiu, Tom Heskes: Learning from Multiple Annotators with Gaussian Processes. ICANN (2) 2011: 159-164
- [c39]Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes: Learning2Reason. Calculemus/MKM 2011: 298-300
- [c38]Ali Bahramisharif, Marcel A. J. van Gerven, Jan-Mathijs Schoffelen, Zoubin Ghahramani, Tom Heskes: The Dynamic Beamformer. MLINI 2011: 148-155
- [c37]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
- [c36]Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf: On Causal Discovery with Cyclic Additive Noise Models. NIPS 2011: 639-647
- [c35]Evgeni Tsivtsivadze, Josef Urban, Herman Geuvers, Tom Heskes: Semantic Graph Kernels for Automated Reasoning. SDM 2011: 795-803
- [c34]Tom Claassen, Tom Heskes: A Logical Characterization of Constraint-Based Causal Discovery. UAI 2011: 135-144
- [i1]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)
- 2010
- [j29]Adriana Birlutiu, Perry Groot, Tom Heskes: Multi-task preference learning with an application to hearing aid personalization. Neurocomputing 73(7-9): 1177-1185 (2010)
- [j28]Botond Cseke, Tom Heskes: Improving posterior marginal approximations in latent Gaussian models. Journal of Machine Learning Research - Proceedings Track 9: 121-128 (2010)
- [j27]Marcel van Gerven, Floris P. de Lange, Tom Heskes: Neural Decoding with Hierarchical Generative Models. Neural Computation 22(12): 3127-3142 (2010)
- [j26]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)
- [c33]Perry Groot, Adriana Birlutiu, Tom Heskes: Bayesian Monte Carlo for the Global Optimization of Expensive Functions. ECAI 2010: 249-254
- [c32]Tom Claassen, Tom Heskes: Causal discovery in multiple models from different experiments. NIPS 2010: 415-423
- [r1]
- [e1]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
2000 – 2009
- 2009
- [j25]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)
- [j24]Marcel van Gerven, Christian Hesse, Ole Jensen, Tom Heskes: Interpreting single trial data using groupwise regularisation. NeuroImage 46(3): 665-676 (2009)
- [j23]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)
- [c31]Ali Bahramisharif, Marcel van Gerven, Tom Heskes: Exploring the impact of alternative feature representations on BCI classification. ESANN 2009
- [c30]Adriana Birlutiu, Perry Groot, Tom Heskes: Multi-task Preference learning with Gaussian Processes. ESANN 2009
- [c29]Marcel van Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes: Bayesian Source Localization with the Multivariate Laplace Prior. NIPS 2009: 1901-1909
- 2008
- [c28]
- 2007
- [j22]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)
- [j21]Bart Bakker, Tom Heskes: Learning and approximate inference in dynamic hierarchical models. Computational Statistics & Data Analysis 52(2): 821-839 (2007)
- [c27]José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. NIPS 2007
- [c26]Adriana Birlutiu, Tom Heskes: Expectation Propagation for Rating Players in Sports Competitions. PKDD 2007: 374-381
- 2006
- [j20]Tom Heskes: Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies. J. Artif. Intell. Res. (JAIR) 26: 153-190 (2006)
- [j19]Onno Zoeter, Tom Heskes: Deterministic approximate inference techniques for conditionally Gaussian state space models. Statistics and Computing 16(3): 279-292 (2006)
- [c25]Rasa Jurgelenaite, Tom Heskes: EM Algorithm for Symmetric Causal Independence Models. ECML 2006: 234-245
- [c24]Rasa Jurgelenaite, Tom Heskes: Symmetric Causal Independence Models for Classification. Probabilistic Graphical Models 2006: 163-170
- 2005
- [j18]Alexander Ypma, Tom Heskes: Novel approximations for inference in nonlinear dynamical systems using expectation propagation. Neurocomputing 69(1-3): 85-99 (2005)
- [j17]Onno Zoeter, Tom Heskes: Change Point Problems in Linear Dynamical Systems. Journal of Machine Learning Research 6: 1999-2026 (2005)
- [c23]Tom Heskes, Bert de Vries: Incremental Utility Elicitation for Adaptive Personalization. BNAIC 2005: 127-134
- [c22]Rasa Jurgelenaite, Peter J. F. Lucas, Tom Heskes: Use of the Noisy Threshold Function in Building Bayesian Networks. BNAIC 2005: 158-165
- [c21]
- 2004
- [j16]Tom Heskes: On the Uniqueness of Loopy Belief Propagation Fixed Points. Neural Computation 16(11): 2379-2413 (2004)
- [c20]Alexander Ypma, Tom Heskes: Novel approximations for inference and learning in nonlinear dynamical systems. ESANN 2004: 361-366
- 2003
- [j15]Bart Bakker, Tom Heskes: Task Clustering and Gating for Bayesian Multitask Learning. Journal of Machine Learning Research 4: 83-99 (2003)
- [j14]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)
- [j13]Bart Bakker, Tom Heskes: Clustering ensembles of neural network models. Neural Networks 16(2): 261-269 (2003)
- [j12]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)
- [c19]
- [c18]
- [c17]Tom Heskes, Kees Albers, Bert Kappen: Approximate Inference and Constrained Optimization. UAI 2003: 313-320
- 2002
- [j11]Tom Heskes, Bart Bakker, Bert Kappen: Approximate algorithms for neural-Bayesian approaches. Theor. Comput. Sci. 287(1): 219-238 (2002)
- [c16]
- [c15]Alexander Ypma, Tom Heskes: Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models. WEBKDD 2002: 35-49
- [c14]Tom Heskes: Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. NIPS 2002: 343-350
- [c13]
- [c12]Tom Heskes, Onno Zoeter: Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks. UAI 2002: 216-223
- [c11]
- 2001
- [j10]Tom Heskes: Self-organizing maps, vector quantization, and mixture modeling. IEEE Transactions on Neural Networks 12(6): 1299-1305 (2001)
- 2000
- [j9]Piërre van de Laar, Tom Heskes: Input selection based on an ensemble. Neurocomputing 34(1-4): 227-238 (2000)
- [j8]Tom Heskes: On "Natural" Learning and Pruning in Multilayered Perceptrons. Neural Computation 12(4): 881-901 (2000)
- [c10]
- [c9]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
- [c8]Tom Heskes, Jan-Joost Spanjers, Wim Wiegerinck: EM Algorithms for Self-Organizing Maps. IJCNN (6) 2000: 9-14
1990 – 1999
- 1999
- [j7]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)
- [j6]Piërre van de Laar, Tom Heskes: Pruning Using Parameter and Neuronal Metrics. Neural Computation 11(4): 977-993 (1999)
- [c7]
- 1998
- [j5]Tom Heskes: Bias/Variance Decompositions for Likelihood-Based Estimators. Neural Computation 10(6): 1425-1433 (1998)
- [c6]Tom Heskes: Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach. ICML 1998: 233-241
- 1997
- [j4]Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen: Task-Dependent Learning of Attention. Neural Networks 10(6): 981-992 (1997)
- [c5]Piërre van de Laar, Stan C. A. M. Gielen, Tom Heskes: Input Selection with Partial Retraining. ICANN 1997: 469-474
- [c4]
- 1996
- [j3]
- [j2]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)
- [c3]
- [c2]
- 1994
- [c1]
- 1992
- [j1]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)
Coauthor Index
data released under the ODC-BY 1.0 license. See also our legal information page
last updated on 2013-05-09 03:17 CEST by the dblp team