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ORCIDTom Heskes
Tom M. Heskes
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- affiliation: Radboud University Nijmegen, NL
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Journal Articles
- 2024
[j69]Laurens Sluijterman
, Eric Cator, Tom Heskes:
Optimal training of Mean Variance Estimation neural networks. Neurocomputing 597: 127929 (2024)- [j68]Roel Bouman, Zaharah Bukhsh, Tom Heskes:
Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need? J. Mach. Learn. Res. 25: 105:1-105:34 (2024) - [j67]Laurens Sluijterman, Eric Cator, Tom Heskes:
How to evaluate uncertainty estimates in machine learning for regression? Neural Networks 173: 106203 (2024) - 2023
- [j66]Lisandro Arturo Jimenez-Roa, Tom Heskes, Tiedo Tinga, Mariëlle Stoelinga:
Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms. IEEE Trans. Dependable Secur. Comput. 20(4): 3317-3327 (2023) - 2022
- [j65]Yuliya Shapovalova, Tom Heskes, Tjeerd Dijkstra:
Non-parametric synergy modeling of chemical compounds with Gaussian processes. BMC Bioinform. 23(1): 14 (2022) - 2021
- [j64]Errol Zalmijn, Tom Heskes, Tom Claassen:
Spectral Ranking of Causal Influence in Complex Systems. Entropy 23(3): 369 (2021) - [j63]Yordan P. Raykov, Luc J. W. Evers, Reham Badawy, Bastiaan R. Bloem, Tom M. Heskes, Marjan J. Meinders, Kasper Claes, Max A. Little:
Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life. IEEE J. Biomed. Health Informatics 25(6): 2293-2304 (2021) - 2019
- [j62]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Large-scale local causal inference of gene regulatory relationships. Int. J. Approx. Reason. 115: 50-68 (2019) - [j61]Payam Piray, Amir Dezfouli, Tom Heskes, Michael J. Frank, Nathaniel D. Daw:
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Comput. Biol. 15(6) (2019) - [j60]Ruifei Cui, Perry Groot, Tom Heskes:
Learning causal structure from mixed data with missing values using Gaussian copula models. Stat. Comput. 29(2): 311-333 (2019) - [j59]Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes:
A novel Bayesian approach for latent variable modeling from mixed data with missing values. Stat. Comput. 29(5): 977-993 (2019) - [j58]Ridho Rahmadi, Perry Groot, Tom Heskes:
Stable Specification Search in Structural Equation Models with Latent Variables. ACM Trans. Intell. Syst. Technol. 10(5): 48:1-48:23 (2019) - 2018
- [j57]Bram Thijssen, Tjeerd Dijkstra, Tom Heskes, Lodewyk F. A. Wessels:
Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates. Bioinform. 34(5): 803-811 (2018) - [j56]Ridho Rahmadi, Perry Groot, Tom Heskes:
The stablespec package for causal discovery on cross-sectional and longitudinal data in R. Neurocomputing 275: 2440-2443 (2018) - [j55]Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes:
A scalable preference model for autonomous decision-making. Mach. Learn. 107(6): 1039-1068 (2018) - 2017
- [j54]Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes:
Causality on cross-sectional data: Stable specification search in constrained structural equation modeling. Appl. Soft Comput. 52: 687-698 (2017) - [j53]Francesco Del Carratore, Andris Jankevics, Rob Eisinga, Tom Heskes, Fangxin Hong, Rainer Breitling:
RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets. Bioinform. 33(17): 2774-2775 (2017) - [j52]Rob Eisinga, Tom Heskes, Ben Pelzer, Manfred te Grotenhuis:
Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers. BMC Bioinform. 18(1): 68:1-68:18 (2017) - [j51]Elena Sokolova, Daniel von Rhein, Jilly Naaijen, Perry Groot, Tom Claassen, Jan K. Buitelaar, Tom Heskes:
Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD. Int. J. Data Sci. Anal. 3(2): 105-119 (2017) - [j50]Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, Tom Heskes:
Multi-Domain Transfer Component Analysis for Domain Generalization. Neural Process. Lett. 46(3): 845-855 (2017) - 2016
- [j49]Bram Thijssen, T. M. H. Dijkstra, Tom Heskes, Lodewyk F. A. Wessels:
BCM: toolkit for Bayesian analysis of Computational Models using samplers. BMC Syst. Biol. 10: 100 (2016) - 2015
- [j48]Christiaan A. de Leeuw, Joris M. Mooij, Tom Heskes, Danielle Posthuma:
MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLoS Comput. Biol. 11(4) (2015) - [j47]Max Hinne, Ronald J. Janssen, Tom Heskes, Marcel A. J. van Gerven:
Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates. PLoS Comput. Biol. 11(11) (2015) - [j46]Adriana Birlutiu, Florence d'Alché-Buc, Tom Heskes:
A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions. IEEE ACM Trans. Comput. Biol. Bioinform. 12(3): 538-550 (2015) - 2014
- [j45]Syed Abbas, Tjeerd Dijkstra, Tom Heskes:
A comparative study of cell classifiers for image-based high-throughput screening. BMC Bioinform. 15: 342 (2014) - [j44]Tom Heskes, Rob Eisinga, Rainer Breitling:
A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments. BMC Bioinform. 15: 367 (2014) - [j43]Ronald J. Janssen, Max Hinne, Tom Heskes, Marcel A. J. van Gerven:
Quantifying uncertainty in brain network measures using Bayesian connectomics. Frontiers Comput. Neurosci. 8: 126 (2014) - [j42]Sanne Schoenmakers, Umut Güçlü, Marcel van Gerven, Tom Heskes:
Gaussian mixture models and semantic gating improve reconstructions from human brain activity. Frontiers Comput. Neurosci. 8: 173 (2014) - [j41]Jesse Alama, Tom Heskes, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban:
Premise Selection for Mathematics by Corpus Analysis and Kernel Methods. J. Autom. Reason. 52(2): 191-213 (2014) - [j40]Max Hinne, Luca Ambrogioni, Ronald J. Janssen, Tom Heskes, Marcel A. J. van Gerven:
Structurally-informed Bayesian functional connectivity analysis. NeuroImage 86: 294-305 (2014) - 2013
- [j39]Adriana Birlutiu, Perry Groot, Tom Heskes:
Efficiently learning the preferences of people. Mach. Learn. 90(1): 1-28 (2013) - [j38]Diego Vidaurre, Marcel A. J. van Gerven, Concha Bielza, Pedro Larrañaga, Tom Heskes:
Bayesian Sparse Partial Least Squares. Neural Comput. 25(12): 3318-3339 (2013) - [j37]Max Hinne, Tom Heskes, Christian F. Beckmann, Marcel A. J. van Gerven:
Bayesian inference of structural brain networks. NeuroImage 66: 543-552 (2013) - [j36]Sanne Schoenmakers, Markus Barth, Tom Heskes, Marcel van Gerven:
Linear reconstruction of perceived images from human brain activity. NeuroImage 83: 951-961 (2013) - 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 Comput. Biol. 8(4) (2012) - 2011
- [j34]Botond Cseke, Tom Heskes:
Properties of Bethe Free Energies and Message Passing in Gaussian Models. J. Artif. Intell. Res. 41: 1-24 (2011) - [j33]Botond Cseke, Tom Heskes:
Approximate Marginals in Latent Gaussian Models. J. Mach. Learn. Res. 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 Trans. Speech Audio Process. 19(4): 811-821 (2011) - [j30]Marco Baglietto, Lubica Benusková, Ivo Bukovsky, Tianping Chen, Tom Heskes, Kazushi Ikeda, Fakhri Karray, Rhee Man Kil, Robert Legenstein, Jinhu Lu, Yunqian Ma, Malik Magdon-Ismail, Michael G. Paulin, Robi Polikar, Danil V. Prokhorov, Marco A. Wiering, Vicente Zarzoso:
Editorial: One Year as EiC, and Editorial-Board Changes at TNN. IEEE Trans. Neural Networks 22(1): 1-7 (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]Marcel van Gerven, Floris P. de Lange, Tom Heskes:
Neural Decoding with Hierarchical Generative Models. Neural Comput. 22(12): 3127-3142 (2010) - [j27]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) - 2009
- [j26]Rasa Jurgelenaite, Tjeerd Dijkstra, Clemens H. M. Kocken, Tom Heskes:
Gene regulation in the intraerythrocytic cycle of Plasmodium falciparum. Bioinform. 25(12): 1484-1491 (2009) - [j25]Marcel van Gerven, Christian Hesse, Ole Jensen, Tom Heskes:
Interpreting single trial data using groupwise regularisation. NeuroImage 46(3): 665-676 (2009) - [j24]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) - 2007
- [j23]Marcel van Gerven, Rasa Jurgelenaite, Babs G. Taal, Tom Heskes, Peter J. F. Lucas:
Predicting carcinoid heart disease with the noisy-threshold classifier. Artif. Intell. Medicine 40(1): 45-55 (2007) - [j22]Bart Bakker, Tom Heskes:
Learning and approximate inference in dynamic hierarchical models. Comput. Stat. Data Anal. 52(2): 821-839 (2007) - 2006
- [j21]Tom Heskes:
Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies. J. Artif. Intell. Res. 26: 153-190 (2006) - [j20]Onno Zoeter, Tom Heskes:
Deterministic approximate inference techniques for conditionally Gaussian state space models. Stat. Comput. 16(3): 279-292 (2006) - 2005
- [j19]Alexander Ypma, Tom Heskes:
Novel approximations for inference in nonlinear dynamical systems using expectation propagation. Neurocomputing 69(1-3): 85-99 (2005) - [j18]Onno Zoeter, Tom Heskes:
Change Point Problems in Linear Dynamical Systems. J. Mach. Learn. Res. 6: 1999-2026 (2005) - 2004
- [j17]Tom Heskes:
On the Uniqueness of Loopy Belief Propagation Fixed Points. Neural Comput. 16(11): 2379-2413 (2004) - 2003
- [j16]Bart Bakker, Tom Heskes:
Task Clustering and Gating for Bayesian Multitask Learning. J. Mach. Learn. Res. 4: 83-99 (2003) - [j15]Tom Heskes, Jan-Joost Spanjers, Bart Bakker, Wim Wiegerinck:
Optimising newspaper sales using neural-Bayesian technology. Neural Comput. Appl. 12(3-4): 212-219 (2003) - [j14]Bart Bakker, Tom Heskes:
Clustering ensembles of neural network models. Neural Networks 16(2): 261-269 (2003) - [j13]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) - 2002
- [j12]Tom Heskes, Bart Bakker, Bert Kappen:
Approximate algorithms for neural-Bayesian approaches. Theor. Comput. Sci. 287(1): 219-238 (2002) - 2001
- [j11]Tom Heskes:
Self-organizing maps, vector quantization, and mixture modeling. IEEE Trans. Neural Networks 12(6): 1299-1305 (2001) - 2000
- [j10]Piërre van de Laar, Tom Heskes:
Input selection based on an ensemble. Neurocomputing 34(1-4): 227-238 (2000) - [j9]Tom Heskes:
On "Natural" Learning and Pruning in Multilayered Perceptrons. Neural Comput. 12(4): 881-901 (2000) - 1999
- [j8]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) - [j7]Piërre van de Laar, Tom Heskes:
Pruning Using Parameter and Neuronal Metrics. Neural Comput. 11(4): 977-993 (1999) - 1998
- [j6]Tom Heskes:
Bias/Variance Decompositions for Likelihood-Based Estimators. Neural Comput. 10(6): 1425-1433 (1998) - 1997
- [j5]Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen:
Task-Dependent Learning of Attention. Neural Networks 10(6): 981-992 (1997) - 1996
- [j4]Tom Heskes:
Transition times in self-organizing maps. Biol. Cybern. 75(1): 49-57 (1996) - [j3]Wim Wiegerinck, Tom Heskes:
How Dependencies between Successive Examples Affect On-Line Learning. Neural Comput. 8(8): 1743-1765 (1996) - [j2]Tom Heskes, Wim Wiegerinck:
A theoretical comparison of batch-mode, on-line, cyclic, and almost-cyclic learning. IEEE Trans. Neural Networks 7(4): 919-925 (1996) - 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)
Conference and Workshop Papers
- 2023
- [c97]Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen:
Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. CLeaR 2023: 707-725 - 2022
- [c96]Jelle Piepenbrock, Tom Heskes, Mikolás Janota, Josef Urban:
Guiding an Automated Theorem Prover with Neural Rewriting. IJCAR 2022: 597-617 - 2020
- [c95]Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen:
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. NeurIPS 2020 - [c94]Konrad P. Mielke, Tom Claassen, Mark A. J. Huijbregts, Aafke M. Schipper, Tom M. Heskes:
Discovering cause-effect relationships in spatial systems with a known direction based on observational data. PGM 2020: 305-316 - [c93]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. UAI 2020: 1049-1058 - 2018
- [c92]Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes:
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. PGM 2018: 37-48 - [c91]Ruifei Cui, Perry Groot, Moritz Schauer, Tom Heskes:
Learning the Causal Structure of Copula Models with Latent Variables. UAI 2018: 188-197 - [c90]Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes:
Bigger Buffer k-d Trees on Multi-Many-Core Systems. VECPAR 2018: 202-214 - 2017
- [c89]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. AISTATS 2017: 1523-1531 - [c88]Ruifei Cui, Perry Groot, Tom Heskes:
Robust Estimation of Gaussian Copula Causal Structure from Mixed Data with Missing Values. ICDM 2017: 835-840 - [c87]Fabian Gieseke, Kai Lars Polsterer, Ashish Mahabal, Christian Igel, Tom Heskes:
Massively-parallel best subset selection for ordinary least-squares regression. SSCI 2017: 1-8 - 2016
- [c86]Terrence W. Deacon, Tom Heskes, Stefan Leijnen:
Exploring Constraint: Simulating Self-Organization and Autogenesis in the Autogenic Automaton. ALIFE 2016: 68-75 - [c85]Tom Heskes:
Causal Discovery from Big Data - Mission (Im)possible?. ICAART (1) 2016: 7 - [c84]Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, I. W. M. van Uden, Frank-Erik de Leeuw, Elena Marchiori, Bram van Ginneken, Bram Platel:
Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation. ISBI 2016: 1414-1417 - [c83]Koen Vijverberg, Mohsen Ghafoorian, Inge W. M. van Uden, Frank-Erik de Leeuw, Bram Platel, Tom Heskes:
A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation. Medical Imaging: Computer-Aided Diagnosis 2016: 97851C - [c82]Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, Tom Heskes:
Computing Lower and Upper Bounds on the Probability of Causal Statements. Probabilistic Graphical Models 2016: 487-498 - [c81]Ruifei Cui, Perry Groot, Tom Heskes:
Copula PC Algorithm for Causal Discovery from Mixed Data. ECML/PKDD (2) 2016: 377-392 - 2015
- [c80]Elena Sokolova, Perry Groot, Tom Claassen, Daniel von Rhein, Jan K. Buitelaar, Tom Heskes:
Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data. AIME 2015: 177-181 - [c79]Laurens Wiel, Tom Heskes, Evgeni Levin:
KeCo: Kernel-Based Online Co-agreement Algorithm. Discovery Science 2015: 308-315 - [c78]Fabian Gieseke, Tapio Pahikkala, Tom Heskes:
Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. IDA 2015: 95-107 - [c77]Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, Tom Heskes:
Domain Generalization Based on Transfer Component Analysis. IWANN (1) 2015: 325-334 - [c76]Mohsen Ghafoorian, Nico Karssemeijer, Inge van Uden, Frank-Erik de Leeuw, Tom Heskes, Elena Marchiori, Bram Platel:
Small white matter lesion detection in cerebral small vessel disease. Medical Imaging: Computer-Aided Diagnosis 2015: 941411 - [c75]Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes:
Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling. AALTD@PKDD/ECML 2015 - [c74]Sanne Schoenmakers, Tom Heskes, Marcel van Gerven:
Hidden Markov Models for Reading Words from the Human Brain. PRNI 2015: 89-92 - 2014
- [c73]Binyam Gebrekidan Gebre, Onno Crasborn, Peter Wittenburg, Sebastian Drude, Tom Heskes:
Unsupervised Feature Learning for Visual Sign Language Identification. ACL (2) 2014: 370-376 - [c72]Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes, Sebastian Drude:
Motion history images for online speaker/signer diarization. ICASSP 2014: 1537-1541 - [c71]Mike Koeman, Tom Heskes:
Mutual Information Estimation with Random Forests. ICONIP (2) 2014: 524-531 - [c70]Binyam Gebrekidan Gebre, Peter Wittenburg, Sebastian Drude, Marijn Huijbregts, Tom Heskes:
Speaker diarization using gesture and speech. INTERSPEECH 2014: 582-586 - [c69]Elena Sokolova, Perry Groot, Tom Claassen, Tom Heskes:
Causal Discovery from Databases with Discrete and Continuous Variables. Probabilistic Graphical Models 2014: 442-457 - [c68]Adriana Birlutiu, Tom Heskes:
Using Topology Information for Protein-Protein Interaction Prediction. PRIB 2014: 10-22 - [c67]Sanne Schoenmakers, Marcel van Gerven, Tom Heskes:
Gaussian mixture models improve fMRI-based image reconstruction. PRNI 2014: 1-4 - 2013
- [c66]Binyam Gebrekidan Gebre, Marcos Zampieri, Peter Wittenburg, Tom Heskes:
Improving Native Language Identification with TF-IDF Weighting. BEA@NAACL-HLT 2013: 216-223 - [c65]Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes:
Automatic Signer Diarization - The Mover Is the Signer Approach. CVPR Workshops 2013: 283-287 - [c64]Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes:
The gesturer is the speaker. ICASSP 2013: 3751-3755 - [c63]Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes:
Automatic sign language identification. ICIP 2013: 2626-2630 - [c62]Tom Claassen, Tom Heskes:
Bayesian Probabilities for Constraint-Based Causal Discovery. IJCAI 2013: 2992-2996 - [c61]Evgeni Tsivtsivadze, Tom Heskes, Armand Paauw:
Multi-view Multi-class Classification for Identification of Pathogenic Bacterial Strains. MCS 2013: 61-72 - [c60]Evgeni Tsivtsivadze, Hanneke Borgdorff, Janneke van de Wijgert, Frank H. J. Schuren, Rita Verhelst, Tom Heskes:
Neighborhood Co-regularized Multi-view Spectral Clustering of Microbiome Data. PSL 2013: 80-90 - [c59]Tom Claassen, Joris M. Mooij, Tom Heskes:
Learning Sparse Causal Models is not NP-hard. UAI 2013 - [c58]Joris M. Mooij, Tom Heskes:
Cyclic Causal Discovery from Continuous Equilibrium Data. UAI 2013 - 2012
- [c57]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 - [c56]Tom de Ruijter, Evgeni Tsivtsivadze, Tom Heskes:
Online Co-regularized Algorithms. Discovery Science 2012: 184-193 - [c55]Marcel A. J. van Gerven, Tom Heskes:
A Linear Gaussian Framework for Decoding of Perceived Images. PRNI 2012: 1-4 - [c54]Tom Claassen, Tom Heskes:
A Bayesian Approach to Constraint Based Causal Inference. UAI 2012: 207-216 - 2011
- [c53]Tom Claassen, Tom Heskes:
A structure independent algorithm for causal discovery. ESANN 2011 - [c52]John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl:
Learning of causal relations. ESANN 2011 - [c51]Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes:
Multi-output Ranking for Automated Reasoning. KDIR 2011: 42-51 - [c50]Marcel van Gerven, Eric Maris, Tom Heskes:
A Markov Random Field Approach to Neural Encoding and Decoding. ICANN (2) 2011: 1-8 - [c49]Perry Groot, Adriana Birlutiu, Tom Heskes:
Learning from Multiple Annotators with Gaussian Processes. ICANN (2) 2011: 159-164 - [c48]Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes:
Learning2Reason. Calculemus/MKM 2011: 298-300 - [c47]Ali Bahramisharif, Marcel A. J. van Gerven, Jan-Mathijs Schoffelen, Zoubin Ghahramani, Tom Heskes:
The Dynamic Beamformer. MLINI 2011: 148-155 - [c46]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 - [c45]Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf:
On Causal Discovery with Cyclic Additive Noise Models. NIPS 2011: 639-647 - [c44]Evgeni Tsivtsivadze, Josef Urban, Herman Geuvers, Tom Heskes:
Semantic Graph Kernels for Automated Reasoning. SDM 2011: 795-803 - [c43]Tom Claassen, Tom Heskes:
A Logical Characterization of Constraint-Based Causal Discovery. UAI 2011: 135-144 - 2010
- [c42]Perry Groot, Adriana Birlutiu, Tom Heskes:
Bayesian Monte Carlo for the Global Optimization of Expensive Functions. ECAI 2010: 249-254 - [c41]Tom Claassen, Tom Heskes:
Causal discovery in multiple models from different experiments. NIPS 2010: 415-423 - [c40]Botond Cseke, Tom Heskes:
Improving posterior marginal approximations in latent Gaussian models. AISTATS 2010: 121-128 - 2009
- [c39]Ali Bahramisharif, Marcel van Gerven, Tom Heskes:
Exploring the impact of alternative feature representations on BCI classification. ESANN 2009 - [c38]Adriana Birlutiu, Perry Groot, Tom Heskes:
Multi-task Preference learning with Gaussian Processes. ESANN 2009 - [c37]Marcel van Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes:
Bayesian Source Localization with the Multivariate Laplace Prior. NIPS 2009: 1901-1909 - 2008
- [c36]Botond Cseke, Tom Heskes:
Bounds on the Bethe Free Energy for Gaussian Networks. UAI 2008: 97-104 - 2007
- [c35]José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes:
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. NIPS 2007: 649-656 - [c34]Adriana Birlutiu, Tom Heskes:
Expectation Propagation for Rating Players in Sports Competitions. PKDD 2007: 374-381 - 2006
- [c33]Rasa Jurgelenaite, Tom Heskes:
EM Algorithm for Symmetric Causal Independence Models. ECML 2006: 234-245 - [c32]Rasa Jurgelenaite, Tom Heskes:
Symmetric Causal Independence Models for Classification. Probabilistic Graphical Models 2006: 163-170 - 2005
- [c31]Onno Zoeter, Tom Heskes:
Gaussian Quadrature Based Expectation Propagation. AISTATS 2005: 445-452 - [c30]Tom Heskes, Bert de Vries:
Incremental Utility Elicitation for Adaptive Personalization. BNAIC 2005: 127-134 - [c29]Rasa Jurgelenaite, Peter J. F. Lucas, Tom Heskes:
Use of the Noisy Threshold Function in Building Bayesian Networks. BNAIC 2005: 158-165 - [c28]Onno Zoeter, Tom Heskes:
Gaussian Quadrature Based Expectation Propagation. BNAIC 2005: 407 - [c27]Rasa Jurgelenaite, Peter J. F. Lucas, Tom Heskes:
Exploring the Noisy Threshold Function in Designing Bayesian Networks. SGAI Conf. 2005: 133-146 - 2004
- [c26]Alexander Ypma, Tom Heskes:
Novel approximations for inference and learning in nonlinear dynamical systems. ESANN 2004: 361-366 - 2003
- [c25]Tom Heskes, Onno Zoeter:
Generalized belief propagation for approximate inference in hybrid Bayesian networks. AISTATS 2003: 132-140 - [c24]Onno Zoeter, Tom Heskes:
Multi-scale Switching Linear Dynamical Systems. ICANN 2003: 562-572 - [c23]Tom Heskes, Onno Zoeter, Wim Wiegerinck:
Approximate Expectation Maximization. NIPS 2003: 353-360 - [c22]Alexander Ypma, Tom Heskes:
Iterated extended Kalman smoothing with expectation-propagation. NNSP 2003: 219-228 - [c21]Tom Heskes, Kees Albers, Bert Kappen:
Approximate Inference and Constrained Optimization. UAI 2003: 313-320 - 2002
- [c20]Bart Bakker, Tom Heskes:
Model Clustering for Neural Network Ensembles. ICANN 2002: 383-388 - [c19]Alexander Ypma, Tom Heskes:
Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models. WEBKDD 2002: 35-49 - [c18]Tom Heskes:
Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. NIPS 2002: 343-350 - [c17]Wim Wiegerinck, Tom Heskes:
Fractional Belief Propagation. NIPS 2002: 438-445 - [c16]Tom Heskes, Onno Zoeter:
Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks. UAI 2002: 216-223 - [c15]Wim Wiegerinck, Tom Heskes:
IPF for Discrete Chain Factor Graphs. UAI 2002: 560-567 - 2000
- [c14]Bart J. Bakker, Bert Kappen, Tom Heskes:
Survival Analysis: A Neural-Bayesian Approach. ANNIMAB 2000: 162-167 - [c13]Tom Heskes:
Empirical Bayes for Learning to Learn. ICML 2000: 367-374 - [c12]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 - [c11]Tom Heskes, Jan-Joost Spanjers, Wim Wiegerinck:
EM Algorithms for Self-Organizing Maps. IJCNN (6) 2000: 9-14 - 1999
- [c10]Bart Bakker, Tom Heskes:
Model clustering by deterministic annealing. ESANN 1999: 87-92 - 1998
- [c9]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
- [c8]Piërre van de Laar, Stan C. A. M. Gielen, Tom Heskes:
Input Selection with Partial Retraining. ICANN 1997: 469-474 - [c7]Tom Heskes:
Selecting Weighting Factors in Logarithmic Opinion Pools. NIPS 1997: 266-272 - 1996
- [c6]Tom Heskes:
Practical Confidence and Prediction Intervals. NIPS 1996: 176-182 - [c5]Tom Heskes:
Balancing Between Bagging and Bumping. NIPS 1996: 466-472 - 1995
- [c4]Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen:
A Neural Model of Visual Attention. SNN Symposium on Neural Networks 1995: 111-114 - [c3]André Pastoors, Tom Heskes:
Output Coding and Modularity for Multi-Class Problems. SNN Symposium on Neural Networks 1995: 221-224 - 1994
- [c2]Tom Heskes:
Stochastics of on-line back-propagation. ESANN 1994 - 1993
- [c1]Tom Heskes, Bert Kappen:
Error potentials for self-organization. ICNN 1993: 1219-1223
Parts in Books or Collections
- 2010
- [p1]Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Tom Heskes:
Co-Regularized Least-Squares for Label Ranking. Preference Learning 2010: 107-123
Editorship
- 2015
- [e2]Marina Meila, Tom Heskes:
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, July 12-16, 2015, Amsterdam, The Netherlands. AUAI Press 2015, ISBN 978-0-9966431-0-8 [contents] - 2010
- [e1]Tjeerd Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori, Tom Heskes:
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 [contents]
Reference Works
- 2017
- [r2]Tom Heskes:
Expectation Propagation. Encyclopedia of Machine Learning and Data Mining 2017: 482-487 - 2010
- [r1]Tom Heskes:
Expectation Propagation. Encyclopedia of Machine Learning 2010: 383-387
Informal and Other Publications
- 2024
- [i42]Binyam Gebre, Karoliina Ranta, Stef van den Elzen, Ernst Kuiper, Thijs Baars, Tom Heskes:
Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities. CoRR abs/2402.16073 (2024) - [i41]Roel Bouman, Linda Schmeitz, Luco Buise, Jacco Heres, Yuliya Shapovalova, Tom Heskes:
Acquiring Better Load Estimates by Combining Anomaly and Change-point Detection in Power Grid Time-series Measurements. CoRR abs/2405.16164 (2024) - [i40]Laurens Sluijterman, Frank Kreuwel, Eric Cator, Tom Heskes:
Composite Quantile Regression With XGBoost Using the Novel Arctan Pinball Loss. CoRR abs/2406.02293 (2024) - 2023
- [i39]Laurens Sluijterman, Eric Cator, Tom Heskes:
Optimal Training of Mean Variance Estimation Neural Networks. CoRR abs/2302.08875 (2023) - [i38]Roel Bouman, Zaharah Bukhsh, Tom Heskes:
Unsupervised anomaly detection algorithms on real-world data: how many do we need? CoRR abs/2305.00735 (2023) - [i37]Laurens Sluijterman, Eric Cator, Tom Heskes:
Likelihood-ratio-based confidence intervals for neural networks. CoRR abs/2308.02221 (2023) - [i36]Charlotte Cambier van Nooten, Tom van de Poll, Sonja Füllhase, Jacco Heres, Tom Heskes, Yuliya Shapovalova:
Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid. CoRR abs/2310.01181 (2023) - [i35]Lisandro Arturo Jimenez-Roa, Tom Heskes, Mariëlle Stoelinga:
Fault Trees, Decision Trees, And Binary Decision Diagrams: A Systematic Comparison. CoRR abs/2310.04448 (2023) - 2022
- [i34]Laurens Sluijterman, Eric Cator, Tom Heskes:
Confident Neural Network Regression with Bootstrapped Deep Ensembles. CoRR abs/2202.10903 (2022) - [i33]Lisandro Arturo Jimenez-Roa, Tom Heskes, Tiedo Tinga, Mariëlle Stoelinga:
Automatic inference of fault tree models via multi-objective evolutionary algorithms. CoRR abs/2204.03743 (2022) - [i32]Jelle Piepenbrock, Josef Urban, Konstantin Korovin, Miroslav Olsák, Tom Heskes, Mikolas Janota:
Machine Learning Meets The Herbrand Universe. CoRR abs/2210.03590 (2022) - 2021
- [i31]Jelle Piepenbrock, Tom Heskes, Mikolás Janota, Josef Urban:
Learning Equational Theorem Proving. CoRR abs/2102.05547 (2021) - [i30]Laurens Sluijterman, Eric Cator, Tom Heskes:
How to Evaluate Uncertainty Estimates in Machine Learning for Regression? CoRR abs/2106.03395 (2021) - [i29]Alex Kolmus, Grégory Baltus, Justin Janquart, Twan van Laarhoven, Sarah Caudill, Tom Heskes:
Swift sky localization of gravitational waves using deep learning seeded importance sampling. CoRR abs/2111.00833 (2021) - [i28]Zhuoran Liu, Zhengyu Zhao, Alex Kolmus, Tijn Berns, Twan van Laarhoven, Tom Heskes, Martha A. Larson:
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples. CoRR abs/2111.13244 (2021) - 2020
- [i27]Yordan P. Raykov, Luc J. W. Evers, Reham Badawy, Bastiaan R. Bloem, Tom M. Heskes, Marjan J. Meinders, Kasper Claes, Max A. Little:
Probabilistic modelling of gait for robust passive monitoring in daily life. CoRR abs/2004.03047 (2020) - [i26]Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen:
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. CoRR abs/2011.01625 (2020) - [i25]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. CoRR abs/2012.10141 (2020) - [i24]Errol Zalmijn, Tom Heskes, Tom Claassen:
Spectral Ranking of Causal Influence in Complex Systems. CoRR abs/2012.13195 (2020) - 2019
- [i23]Sascha Caron, Tom Heskes, Sydney Otten, Bob Stienen:
Constraining the Parameters of High-Dimensional Models with Active Learning. CoRR abs/1905.08628 (2019) - [i22]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Large-Scale Local Causal Inference of Gene Regulatory Relationships. CoRR abs/1909.03818 (2019) - 2018
- [i21]Ridho Rahmadi, Perry Groot, Tom Heskes:
Stable specification search in structural equation model with latent variables. CoRR abs/1805.09527 (2018) - [i20]Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes:
A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values. CoRR abs/1806.04610 (2018) - [i19]Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes:
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. CoRR abs/1809.06827 (2018) - 2017
- [i18]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. CoRR abs/1704.01864 (2017) - 2016
- [i17]Harm Berntsen, Wouter Kuijper, Tom Heskes:
The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection. CoRR abs/1604.04428 (2016) - [i16]Ridho Rahmadi, Perry Groot, Marieke M. H. C. van Rijn, Jan A. J. G. van den Brand, Marianne Heins, Hans Knoop, Tom Heskes:
Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling. CoRR abs/1605.06838 (2016) - [i15]Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Inge van Uden, Clara I. Sánchez, Geert Litjens, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel:
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities. CoRR abs/1610.04834 (2016) - [i14]Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Mayra Bergkamp, Joost Wissink, Jiri Obels, Karlijn Keizer, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel:
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin. CoRR abs/1610.07442 (2016) - 2015
- [i13]Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes:
Causality on Cross-Sectional Data: Stable Specification Search in Constrained Structural Equation Modeling. CoRR abs/1506.05600 (2015) - [i12]Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes:
Bigger Buffer k-d Trees on Multi-Many-Core Systems. CoRR abs/1512.02831 (2015) - 2014
- [i11]Botond Cseke, Tom Heskes:
Properties of Bethe Free Energies and Message Passing in Gaussian Models. CoRR abs/1401.3877 (2014) - 2013
- [i10]Tom Heskes, Onno Zoeter:
Expectation Propogation for approximate inference in dynamic Bayesian networks. CoRR abs/1301.0572 (2013) - [i9]Wim Wiegerinck, Tom Heskes:
IPF for Discrete Chain Factor Graphs. CoRR abs/1301.0613 (2013) - [i8]Evgeni Tsivtsivadze, Tom Heskes:
Semi-supervised Ranking Pursuit. CoRR abs/1307.0846 (2013) - [i7]Tom Claassen, Joris M. Mooij, Tom Heskes:
Learning Sparse Causal Models is not NP-hard. CoRR abs/1309.6824 (2013) - [i6]Joris M. Mooij, Tom Heskes:
Cyclic Causal Discovery from Continuous Equilibrium Data. CoRR abs/1309.6849 (2013) - 2012
- [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
- [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)
Coauthor Index
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