Ka-Hoo Lam,
Ioan Gabriel Bucur,
Pim van Oirschot,
Frank de Graaf,
Eva Strijbis,
Bernard Uitdehaag,
Tom Heskes,
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Personalized monitoring of ambulatory function with a smartphone two-minute walk test in multiple sclerosis.
Multiple Sclerosis Journal,
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The effect of cardiovascular risk on disease progression in de novo Parkinson's disease patients: An observational analysis.
Frontiers in Neurology,
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Specialized versus generic allied health therapy and the risk of Parkinson's disease complications.
Movement Disorders,
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autoRasch: An R Package to Do Semi-Automated Rasch Analysis.
Applied Psychological Measurement,
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2023.
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Lisandro Jimenez-Roa,
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Automatic inference of fault tree models via multi-objective evolutionary algorithms.
IEEE Transactions on Dependable and Secure Computing,
2022.
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Alex Kolmus,
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Justin Janquart,
Twan van Laarhoven,
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Fast sky localization of gravitational waves using deep learning seeded importance sampling.
Physical Review D,
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2022.
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Bernard Uitdehaag,
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Joep Killestein,
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Towards individualized monitoring of cognition in multiple sclerosis in the digital era: a one-year cohort study.
Multiple Sclerosis and Related Disorders,
60:103692,
2022.
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Konrad Mielke,
Aafke Schipper,
Tom Heskes,
Mark Huijbregts,
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Discovering ecological relationships in flowing freshwater ecosystems.
Frontiers in Ecology and Evolution,
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2022.
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Tom Loonen,
Perry Groot,
Sanne Vincenten,
Simone Knuijt,
Jan Groothuis,
Thomas Maal,
Tom Heskes,
Nicol Voermans,
and Baziel van Engelen.
Development and validation of the patient-reported Facial Function Scale for facioscapulohumeral muscular dystrophy.
Disability and Rehabilitation,
45:1530-1535,
2022.
[bib]
Martijn Schilpzand,
Chase Neff,
Jeroen van Dillen,
Bram van Ginneken,
Tom Heskes,
Chris de Korte,
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Automatic placenta localization from ultrasound imaging in a resource-limited setting using a predefined ultrasound acquisition protocol and deep learning.
Ultrasound in Medicine and Biology,
48:663-674,
2022.
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Yuliya Shapovalova,
Tom Heskes,
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Non-parametric synergy modeling of chemical compounds with Gaussian processes.
BMC Bioinformatics,
23:14,
2022.
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Feri Wijayanto,
Karlien Mul,
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Semi-automated Rasch analysis with differential item functioning.
Behavior Research Methods,
2022.
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Jeanne Savage,
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Tom Heskes,
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Understanding the assumptions underlying Mendelian Randomization.
European Journal of Human Genetics,
30:653–660,
2022.
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Fabiola Müller,
Feri Wijayanto,
Harriët Abrahams,
Marieke Gielissen,
Hetty Prinsen,
Annemarie Braamse,
Hanneke van Laarhoven,
Perry Groot,
Tom Heskes,
and Hans Knoop.
Potential mechanisms of the fatigue-reducing effect of cognitive-behavioral therapy in cancer survivors: Three randomized controlled trials.
Psycho-Oncology,
30(9):1476-1484,
2021.
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Adriaan Penson,
Sylvia van Deuren,
Ewald Bronkhorst,
Ellen Keizer,
Tom Heskes,
Marieke Coenen,
Judith Rosmalen,
Wim Tissing,
Helena van der Pal,
Andrica de Vries,
Marry van den Heuvel-Eibrink,
Sebastian Neggers,
Birgitta Versluys,
Marloes Louwerens,
Margriet van der Heiden-van der Loo,
Saskia Pluijm,
Martha Grootenhuis,
Nicole Blijlevens,
Leontien Kremer,
Eline van Dulmen-den Broeder,
Hans Knoop,
and Jacqueline Loonen.
Methodology of the DCCSS later fatigue study: A model to investigate chronic fatigue in long-term survivors of childhood cancer.
BMC Medical Research Methodology,
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2021.
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Feri Wijayanto,
Ioan Gabriel Bucur,
Karlien Mul,
Perry Groot,
Baziel van Engelen,
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Semi-automated Rasch analysis using in-plus-out-of-questionnaire log likelihood.
British Journal of Mathematical and Statistical Psychology,
74:313-339,
2021.
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Errol Zalmijn,
Tom Heskes,
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Spectral ranking of causal influence in complex systems.
Entropy,
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2021.
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Lieneke van den Heuvel,
Luc Evers,
Marjan Meinders,
Bart Post,
Tom Heskes,
Bas Bloem,
and Jesse Krijtje.
Estimating the effect of early treatment initiation in Parkinson's disease using observational data.
Movement Disorders,
36:407-414,
2021.
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Luc Evers,
Yordan Raykov,
Jesse Krijthe,
Ana Lìgia Silva de Lima,
Reham Badawy,
Kasper Claes,
Tom Heskes,
Max Little,
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and Bas Bloem.
Real-life gait performance as a digital biomarker for motor fluctuations: the Parkinson@Home validation study.
Journal of Medical Internet Research,
22:e19068,
2020.
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Simone Lederer,
Tom Heskes,
Kees Albers,
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Investigating the effect of dependence between conditions with Bayesian linear mixed models for motif activity analysis.
PLOS ONE,
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2020.
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Konrad Mielke,
Tom Claassen,
Michela Busana,
Tom Heskes,
Mark Huijbregts,
Kees Koffijberg,
and Aafke Schipper.
Disentangling drivers of spatial autocorrelation in species distribution models.
Ecography,
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2020.
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Yordan Raykov,
Luc Evers,
Bas Bloem,
Tom Heskes,
Marjan Meinders,
Kasper Claes,
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Probabilistic modelling of gait for robust passive monitoring in daily life.
IEEE Journal of Biomedical and Health Informatics,
25(6):2293-2304,
2020.
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Gido Schoenmacker,
Annabeth Groenman,
Elena Sokolova,
Jaap Oosterlaan,
Nanda Rommelse,
Herbert Roeyers,
Bob Oades,
Stephen Faraone,
Barbara Franke,
Tom Heskes,
Alejandro Arias Vasquez,
Tom Claassen,
and Jan Buitelaar.
Role of conduct problems in the relation between Attention-Deficit Hyperactivity disorder, substance use, and gaming.
European Neuropsychopharmacology,
30:102-113,
2020.
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Gido Schoenmacker,
Katre Sakala,
Barbara Franke,
Jan Buitelaar,
Toomas Veidebaum,
Jaanus Harro,
Tom Heskes,
Tom Claassen,
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Identification and validation of risk factors for antisocial behaviour involving police.
Psychiatry Research,
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2020.
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Bram Ton,
Rob Basten,
John Bolte,
Jan Braaksma,
Alessandro di Bucchianico,
Philippe Calseyde,
Frank Grooteman,
Tom Heskes,
Nils Jansen,
Wouter Teeuw,
Tiedo Tinga,
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PrimaVera: Synergising predictive maintenance.
Applied Sciences,
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2020.
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Tom Claassen,
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Inferring the direction of a causal link and estimating its effect via a Bayesian Mendelian randomization approach.
Statistical Methods in Medical Research,
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Ioan Gabriel Bucur,
Tom Claassen,
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Large-scale local causal inference of gene regulatory relationships.
International Journal of Approximate Reasoning,
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2019.
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Sascha Caron,
Tom Heskes,
Sydney Otten,
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Constraining the parameters of high-dimensional models with active learning.
European Physical Journal C,
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2019.
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Ruifei Cui,
Ioan Gabriel Bucur,
Perry Groot,
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A novel Bayesian approach for latent variable modeling from mixed data with missing values.
Statistics and Computing,
29:977-993,
2019.
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Ruifei Cui,
Perry Groot,
and Tom Heskes.
Learning causal structure from mixed data with missing values using Gaussian copula models.
Statistics and Computing,
29(2):311-333,
2019.
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Luc Evers,
Jesse Krijtje,
Marjan Meinders,
Bas Bloem,
and Tom Heskes.
Measuring Parkinson's disease over time: the real-world within-subject reliability of the MDS-UPDRS.
Movement Disorders,
34:1480-1487,
2019.
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Simone Lederer,
Tjeerd Dijkstra,
and Tom Heskes.
Additive dose response models: Defining synergy.
Frontiers in Pharmacology,
10:1384,
2019.
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Payam Piray,
Amir Dezfouli,
Tom Heskes,
Michael J Frank,
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Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.
PLOS Computational Biology,
15(6):e1007043,
2019.
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Ridho Rahmadi,
Perry Groot,
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Stable specification search in structural equation models with latent variables.
Transactions on Intelligent Systems and Technology,
10(5):48,
2019.
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Simone Lederer,
Tjeerd Dijkstra,
and Tom Heskes.
Additive dose response models: Explicit formulation and the Loewe additivity consistency condition.
Frontiers in Pharmacology,
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2018.
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Cognitive behavioural therapy with optional graded exercise therapy in patients with severe fatigue with myotonic dystrophy type 1: a multicentre, single-blind, randomised trial.
The Lancet Neurology,
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2018.
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Markus Peters,
Maytal Saar-Tsechansky,
Wolfgang Ketter,
Sinead Williamson,
Perry Groot,
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A scalable preference model for autonomous decision-making.
Machine Learning,
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2018.
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Ridho Rahmadi,
Perry Groot,
and Tom Heskes.
The stablespec package for causal discovery on cross-sectional and longitudinal data in R.
Neurocomputing,
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2018.
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Ridho Rahmadi,
Perry Groot,
Marieke van Rijn,
Jan van den Brand,
Marianne Heins,
Hans Knoop,
Tom Heskes,
the Alzheimer's Disease Neuroimaging Initiative,
the MASTERPLAN Study Group,
and the OPTIMISTIC consortium.
Causality on longitudinal data: Stable specification search in constrained structural equation modeling.
Statistical Methods in Medical Research,
27(12):3814-3834,
2018.
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Bram Thijssen,
Tjeerd Dijkstra,
Tom Heskes,
and Lodewyk Wessels.
Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates.
Bioinformatics,
34:803-811,
2018.
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Christiaan de Leeuw,
Sven Stringer,
Ilona Dekkers,
Tom Heskes,
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Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure.
Nature Communications,
9:3768,
2018.
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Francesco Del Carratore,
Andris Jankevics,
Rob Eisinga,
Tom Heskes,
Fangxin Hong,
and Rainer Breitling.
RankProd 2.0: a refactored Bioconductor package for detecting differentially expressed features in molecular profiling datasets.
Bioinformatics,
33:2774-2775,
2017.
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Rob Eisinga,
Tom Heskes,
Ben Pelzer,
and Manfred Te Grotenhuis.
Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers.
BMC Bioinformatics,
18:68,
2017.
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Mohsen Ghafoorian,
Nico Karssemeijer,
Tom Heskes,
Mayra Bergkamp,
Joost Wissink,
Jiri Obels,
Karlijn Keizer,
Frank-Erik de Leeuw,
Bram van Ginneken,
Elena Marchiori,
and Bram Platel.
Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin.
NeuroImage Clinical,
14:391-399,
2017.
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Mohsen Ghafoorian,
Nico Karssemeijer,
Tom Heskes,
Inge van Uden,
Clara Sanchez,
Geert Litjens,
Frank-Erik de Leeuw,
Bram van Ginneken,
Elena Marchiori,
and Bram Platel.
Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities.
Scientific Reports,
7:5110,
2017.
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Fabian Gieseke,
Steven Bloemen,
Cas van den Bogaard,
Tom Heskes,
Jonas Kindler,
Richard Scalzo,
Valèrio Ribeiro,
Jan van Roestel,
Paul Groot,
Fang Yuan,
Anais Möller,
and Brad Tucker.
Convolutional neural networks for transient candidate vetting in large-scale surveys.
Monthly Notices of the Royal Astronomical Society,
472:3101-3114,
2017.
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Thomas Grubinger,
Adriana Birlutiu,
Holger Schöner,
Thomas Natschläger,
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Multi-domain transfer component analysis for domain generalization.
Neural Processing Letters,
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2017.
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Ridho Rahmadi,
Perry Groot,
Marianne Heins,
Hans Knoop,
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Causality on cross-sectional data: Stable specification search in constrained structural equation modeling.
Applied Soft Computing,
52:687-698,
2017.
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Elena Sokolova,
Anoek Oerlemans,
Nanda Rommelse,
Perry Groot,
Catharina Hartman,
Jeffrey Glennon,
Tom Claassen,
Tom Heskes,
and Jan Buitelaar.
A causal and mediation analysis of the comorbidity between attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD).
Journal of Autism and Developmental Disorders,
47:1595-1604,
2017.
[bib]
Elena Sokolova,
Daniel von Rhein,
Jilly Naaijen,
Perry Groot,
Tom Claassen,
Jan Buitelaar,
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Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD.
International Journal of Data Science and Analytics,
3:105-119,
2017.
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Botond Cseke,
Andrew Zammit-Mangion,
Tom Heskes,
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Sparse approximate inference for spatio-temporal point process models.
Journal of the American Statistical Association,
111(516):1746-1763,
2016.
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Mohsen Ghafoorian,
Nico Karssemeijer,
Inge van Uden,
Frank Erik de Leeuw,
Tom Heskes,
Elena Marchiori,
and Bram Platel.
Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease.
Medical Physics,
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2016.
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Elena Sokolova,
Perry Groot,
Tom Claassen,
Kimm van Hulzen,
Jeffrey Glennon,
Barbara Franke,
Tom Heskes,
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Statistical evidence suggests that inattention drives hyperactivity/impulsivity in attention deficit-hyperactivity disorder.
PLOS ONE,
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2016.
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Bram Thijssen,
Tjeerd Dijkstra,
Tom Heskes,
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BCM: toolkit for Bayesian analysis of Computational Models using samplers.
BMC Systems Biology,
10(1):100,
2016.
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Christiaan de Leeuw,
Benjamin Neale,
Tom Heskes,
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The statistical properties of gene-set analysis.
Nature Review Genetics,
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2016.
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Adriana Birlutiu,
Florence d'Alché-Buc,
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A Bayesian framework for combining protein and network topology information for predicting protein-protein interactions.
IEEE/ACM Transactions on Computational Biology and Bioinformatics,
12:538-550,
2015.
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Max Hinne,
Matthias Ekman,
Ronald Janssen,
Tom Heskes,
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Probabilistic clustering of the human connectome identifies communities and hubs.
PLOS ONE,
10(1):e0117179,
2015.
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Max Hinne,
Ronald Janssen,
Tom Heskes,
and Marcel van Gerven.
Bayesian estimation of conditional independence graphs improves functional connectivity estimates.
PLOS Computational Biology,
11(11):e1004534,
2015.
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Sanne Schoenmakers,
Umut Güçlü,
Marcel van Gerven,
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Gaussian mixture models and semantic gating improve reconstructions from human brain activity.
Frontiers in Computational Neuroscience,
8(173),
2015.
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Elena Sokolova,
Martine Hoogman,
Perry Groot,
Tom Claassen,
Alejandro Arias Vasquez,
Jan Buitelaar,
Barbara Franke,
and Tom Heskes.
Causal discovery in an adult ADHD data set suggests indirect link between DAT1 genetic variants and striatal brain activation during reward processing.
American Journal of Medical Genetics Part B: Neuropsychiatric Genetics,
9999B:1-8,
2015.
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Christiaan de Leeuw,
Joris Mooij,
Tom Heskes,
and Danielle Posthuma.
MAGMA: Generalized Gene-Set Analysis of GWAS Data.
PLOS Computational Biology,
11(4):e1004219,
2015.
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Syed Saiden Abbas,
Tjeerd Dijkstra,
and Tom Heskes.
A comparative study of cell classifiers for image-based high-throughput screening.
BMC Bioinformatics,
15:342,
2014.
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Jesse Alama,
Tom Heskes,
Daniel Kühlwein,
Evgeni Tsivtsivadze,
and Josef Urban.
Premise selection for mathematics by corpus analysis and kernel methods.
Journal of Automated Reasoning,
52:191-213,
2014.
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Tom Heskes,
Rob Eisinga,
and Rainer Breitling.
A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments.
BMC Bioinformatics,
15:367,
2014.
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Max Hinne,
Luca Ambrogioni,
Ronald Janssen,
Tom Heskes,
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Structurally-informed Bayesian functional connectivity analysis.
NeuroImage,
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2014.
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Max Hinne,
Alex Lenkoski,
Tom Heskes,
and Marcel van Gerven.
Efficient sampling of Gaussian graphical models using conditional Bayes factors.
Stat,
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2014.
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Ronald Janssen,
Max Hinne,
Tom Heskes,
and Marcel van Gerven.
Quantifying uncertainty in brain network measures using Bayesian connectomics.
Frontiers in Computational Neuroscience,
8(126),
2014.
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Frank Koopmans,
Niels Cornelisse,
Tom Heskes,
and Tjeerd Dijkstra.
An empirical Bayesian random censoring threshold model improves detection of differentially abundant proteins.
Journal of Proteome Research,
13:3871-3880,
2014.
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Syed Saiden Abbas,
Tjeerd Dijkstra,
and Tom Heskes.
A direct comparison of visual discrimination of shape and size on a large range of aspect ratios.
Vision Research,
91:84-92,
2013.
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Syed Saiden Abbas,
Tom Heskes,
Onno Zoeter,
and Tjeerd Dijkstra.
A Bayesian psychophysical model for angular variables.
Journal of Mathematical Psychology,
57:134-139,
2013.
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Adriana Birlutiu,
Perry Groot,
and Tom Heskes.
Efficiently learning the preferences of people.
Machine Learning,
90:1-28,
2013.
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Rob Eisinga,
Rainer Breitling,
and Tom Heskes.
The exact probability distribution of the rank product statistics for replicated experiments.
FEBS Letters,
587(6):677-682,
2013.
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Max Hinne,
Tom Heskes,
Christian Beckmann,
and Marcel van Gerven.
Bayesian inference of structural brain networks.
NeuroImage,
66:543-552,
2013.
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Eleftheria Mavridou,
Joseph Meletiadis,
Pavol Jancura,
Saiden Abbas,
Maiken Arendrup,
Willem Melchers,
Tom Heskes,
Johan Mouton,
and Paul Verweij.
Composite survival index to compare virulence changes in azole-resistant Aspergillus fumigatus clinical isolates.
PLOS ONE,
8(8):e72280,
2013.
[bib]
Sanne Schoenmakers,
Markus Barth,
Tom Heskes,
and Marcel van Gerven.
Linear reconstruction of perceived images from human brain activity.
NeuroImage,
83:951-961,
2013.
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Diego Vidaurre,
Marcel van Gerven,
Concha Bielza,
Pedro Larrañaga,
and Tom Heskes.
Bayesian sparse partial least squares.
Neural Computation,
25:3318-3339,
2013.
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Niels Cornelisse,
Evgeni Tsivtsivadze,
Marieke Meijer,
Tjeerd Dijkstra,
Tom Heskes,
and Matthijs Verhage.
Molecular machines in the synapse: overlapping protein sets control distinct steps in neurosecretion.
PLOS Computational Biology,
8:e1002450,
2012.
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Marcel van Gerven,
Zenas Chao,
and Tom Heskes.
On the decoding of intracranial data using sparse orthonormalized partial least squares.
Journal of Neural Engineering,
9:026017,
2012.
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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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Onno Zoeter and Tom Heskes.
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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Ali Bahramisharif,
Tom Heskes,
Ole Jensen,
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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,
Floris de Lange,
and Tom Heskes.
Dynamic decoding of ongoing perception.
NeuroImage,
53:950-957,
2011.
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Ali Bahramisharif,
Marcel van Gerven,
Tom Heskes,
and Ole Jensen.
Covert attention allows for continuous control of brain-computer interfaces.
European Journal of Neuroscience,
31:1501-1508,
2010.
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Adriana Birlutiu,
Perry Groot,
and Tom Heskes.
Multi-task preference learning with an application to hearing aid personalization.
Neurocomputing,
73:1177-1185,
2010.
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Marcel van Gerven,
Botond Cseke,
Floris de Lange,
and Tom Heskes.
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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Interpreting single trial data using groupwise regularisation.
NeuroImage,
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2009.
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Learning symmetric causal independence models.
Machine Learning,
71:133-153,
2008.
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Kees Albers,
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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,
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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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Deterministic approximate inference techniques for conditionally Gaussian state space models.
Statistics and Computing,
16:279-292,
2006.
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Wim Wiegerinck,
Ole Winther,
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Approximate inference techniques with expectation constraints.
Journal of Statistical Mechanics: Theory and Experiment,
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2005.
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Novel approximations for inference in nonlinear dynamical systems using expectation propagation.
Neurocomputing,
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2005.
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Change point problems in linear dynamical systems.
Journal of Machine Learning Research,
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2005.
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An introduction to neural networks.
In Encyclopedia of Life Support Systems.
2004.
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Improving Cox survival analysis with a neural-Bayesian approach.
Statistics in Medicine,
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2004.
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On the uniqueness of loopy belief propagation fixed points.
Neural Computation,
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2004.
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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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Optimising newspaper sales using neural-Bayesian technology.
Neural Computing and Applications,
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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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Belief networks/Bayesian networks.
In J. Meij, editor, Dealing with the data flow. Mining data, text and multimedia,
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2002.
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Clustering ensembles of neural network models.
Neural Networks,
16:261-269,
2002.
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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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Self-organizing maps, vector quantization, and mixture modeling.
IEEE Transactions on Neural Networks,
12:1299-1305,
2001.
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On natural learning and pruning in multilayered perceptrons.
Neural Computation,
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2000.
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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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Input selection based on an ensemble.
Neurocomputing,
34:227-238,
2000.
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Energy functions for self-organizing maps.
In E. Oja and S. Kaski, editors, Kohonen Maps,
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Elsevier,
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1999.
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Pruning using parameter and neuronal metrics.
Neural Computation,
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1999.
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Tom Heskes,
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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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1998.
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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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Tom Heskes,
and Stan Gielen.
Task-dependent learning of attention.
Neural Networks,
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1997.
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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,
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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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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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Cooling schedules for learning in neural networks.
Physical Review E,
47:4457-4464,
1993.
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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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Eddy Slijpen,
and Bert Kappen.
Learning in neural networks with local minima.
Physical Review A,
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1992.
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Learning processes in neural networks.
Physical Review A,
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Beyond the Markov equivalence class: extending causal discovery under latent confounding.
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Deterioration modeling of sewer pipes via discrete-time Markov chains: A large-scale case study in the Netherlands.
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Guiding an automated theorem prover with neural rewriting.
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Fault trees, decision trees, and binary decision diagrams: A systematic comparison.
In Bruno Castanier,
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MASSIVE: Tractable and robust Bayesian learning of many-dimensional instrumental variable models.
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Proceedings of Machine Learning Research.
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Causal Shapley values: Exploiting causal knowledge to explain individual predictions of complex models.
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Discovering cause-effect relationships in spatial systems with a known direction based on observational data.
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Bigger buffer k-d trees on multi-many-core systems.
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Application of a causal discovery model to study the effect of iron supplementation in children with iron deficiency anemia.
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A Bayesian approach for inferring local causal structure in gene regulatory networks.
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Proceedings of the Ninth International Conference on Probabilistic Graphical Models,
volume 72 of Proceedings of Machine Learning Research,
Prague, Czech Republic,
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Learning the causal structure of copula models with latent variables.
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Robust causal estimation in the large-sample limit without strict faithfulness.
In Aarti Singh and Jerry Zhu, editors,
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Robust estimation of Gaussian copula causal structure from mixed data with missing values.
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Massively-parallel best subset selection for ordinary least-squares regression.
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The artificial mind's eye: resisting adversarials for convolutional neural networks using internal projection.
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2016.
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Copula PC algorithm for causal discovery from mixed data.
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2016.
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Inge van Uden,
Frank Erik de Leeuw,
Elena Marchiori,
Bram van Ginneken,
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Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation.
In 2016 IEEE 13th International Symposium on Biomedical Imaging ISBI,
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2016.
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Exploring constraint: simulating self-organization and autogenesis in the autogenic automaton.
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2016.
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Martine Hoogman,
Perry Groot,
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Computing lower and upper bounds on the probability of causal statements.
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2016.
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Mohsen Ghafoorian,
Inge van Uden,
Frank-Erik de Leeuw,
Bram Platel,
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A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation.
In Proc. SPIE,
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Tapio Pahikkala,
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Batch steepest-descent-mildest-ascent for interactive maximum margin clustering.
In Elisa Fromont,
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volume 9385 of Lecture Notes in Computer Science,
pages 95-107,
2015.
Springer International Publishing.
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Adriana Birlutiu,
Holger Schöner,
Thomas Natschläger,
and Tom Heskes.
Domain Generalization Based on Transfer Component Analysis.
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volume 9094 of Lecture Notes in Computer Science,
pages 325-334,
2015.
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Marianne Heins,
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Causality on longitudinal data: stable specification search in constrained structural equation modeling.
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2015.
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Hidden Markov models for reading words from the human brain.
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2015.
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Tom Claassen,
Daniel von Rhein,
Jan Buitelaar,
and Tom Heskes.
Causal discovery from medical data: dealing with missing values and a mixture of discrete and continuous data.
In John H. Holmes,
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volume 9105 of Lecture Notes in Computer Science,
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KeCo: kernel-based online co-agreement algorithm.
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Using Topology Information for Protein-Protein Interaction Prediction.
In Matteo Comin,
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volume 8626 of Lecture Notes in Computer Science,
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2014.
Springer International Publishing.
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Peter Wittenburg,
Sebastian Drude,
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Unsupervised feature learning for visual sign language identification.
In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics,
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2014.
Association for Computational Linguistics.
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Peter Wittenburg,
Sebastian Drude,
Marijn Huijbregts,
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Speaker diarization using gesture and speech.
In Proceedings of Interspeech 2014: 15th Annual Conference of the International Speech Communication Association,
2014.
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Peter Wittenburg,
Tom Heskes,
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Motion history images for online speaker/signer diarization.
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Markus Peters,
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Fast Laplace approximation for Gaussian processes with a tensor product kernel.
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Mutual information estimation with random forests.
In ChuKiong Loo,
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volume 8835 of Lecture Notes in Computer Science,
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2014.
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Perry Groot,
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Stable specification searches in structural equation modeling using multi-objective evolutionary algorithm.
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2014.
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Marcel van Gerven,
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Gaussian mixture models improve fMRI-based image reconstruction.
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Perry Groot,
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Causal discovery from databases with discrete and continuous variables.
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Bayesian probabilities for constraint-based causal discovery.
In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI),
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2013.
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Learning sparse causal models is not NP-hard.
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Automatic sign language identification.
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Automatic signer diarization - the mover is the signer approach.
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The gesturer is the speaker.
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Marcos Zampieri,
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Improving native language identification with TF-IDF weighting.
In Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications,
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Cyclic causal discovery from continuous equilibrium data.
In UAI 2013, Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence,
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Discovering cyclic causal models from continuous equilibrium data.
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Rita Verhelst,
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Neighborhood Co-regularized Multi-view Spectral Clustering of Microbiome Data.
In Zhi-Hua Zhou and Friedhelm Schwenker, editors,
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Multi-view multi-class classification for identification of pathogenic bacterial strains.
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The dynamic beamformer.
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A Bayesian approach to constraint based causal inference.
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Overview and evaluation of premise selection techniques for large theory mathematics.
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Online co-regularized algorithms.
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Large scale co-regularized ranking.
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Covert attention as a paradigm for subject-independent brain-computer interfacing.
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Irina Rish,
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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.
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Learning semantics for automated reasoning.
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Learning2Reason.
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Multi-output ranking for automated reasoning.
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On causal discovery with cyclic additive noise models.
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Learning of causal relations.
In ESANN 2011,
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Semantic graph kernels for automated reasoning.
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Markov random field approach to neural encoding and decoding.
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pages 1-8,
2011.
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Causal discovery in multiple models from different experiments.
In John Lafferty,
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Richard Zemel,
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pages 415-423,
2010.
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Tom Claassen and Tom Heskes.
Learning causal network structure from multiple (in)dependence models.
In Petri Myllymäki,
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Proceedings of PGM 2010,
pages 81-88,
2010.
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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,
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Bayesian Monte Carlo for the global optimization of expensive functions.
In Helder Coelho,
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Proceedings of ECAI,
pages 249-254,
2010.
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Evgeni Tsivtsivadze and Tom Heskes.
Sparse preference learning.
In Irina Rish,
Alexandru Niculescu-Mizil,
Guillermo Cecchi,
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NIPS Workshop on Practical Application of Sparse Modeling: Open Issues and New Directions,
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Adriana Birlutiu,
Celine Brouard,
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and Marie Szafranski.
Regularized output kernel regression for protein-protein interaction prediction: application to link transfer and transduction.
In Machine Learning in Computational Biology,
2010.
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Marcel van Gerven and Tom Heskes.
Sparse orthonormalized partial least squares.
In BNAIC 2010,
2010.
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Marcel van Gerven,
Floris de Lange,
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A hierarchical generative model for percept reconstruction.
In Human Brain Mapping,
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Ali Bahramisharif,
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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Adriana Birlutiu,
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,
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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),
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Evgeni Tsivtsivadze,
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Kernel principal component ranking: robust ranking on noisy data.
In Eyke Hüllermeier and Johannes Fürnkranz, editors,
ECML/PKDD-Workshop on Preference Learning (PL-09),
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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2008.
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Regulator discovery from gene expression time series of malaria parasites: a hierarchical approach.
In J.C. Platt,
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2008.
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Christian Hesse,
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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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Adriana Birlutiu and Tom Heskes.
Expectation propagation for rating players in sports competitions.
In Joost N. Kok,
Jacek Koronacki,
Ramon López de Mántaras,
Stan Matwin,
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volume 4702 of Lecture Notes in Computer Science,
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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,
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Tom Heskes,
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Using symmetric causal independence models to predict gene expression from sequence data.
In A. Fazel Famili,
Xiaohui Liu,
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2007.
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Daan Holtackers,
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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,
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2006.
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EM Algorithm for Symmetric Causal Independence Models.
In Johannes Fürnkranz,
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2006.
Springer.
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Symmetric causal independence models for classification.
In Proceedings of the Third European Workshop on Probabilistic Graphical Models,
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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),
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Incremental utility elicitation for adaptive personalization.
In K. Verbeeck,
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Peter Lucas,
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Exploring the noisy threshold function in designing Bayesian networks.
In Max Bramer,
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Peter Lucas,
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Use of the noisy threshold function in building Bayesian networks.
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Gaussian quadrature based expectation propagation.
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Approximate Expectation Maximization.
In S. Thrun,
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Novel approximations for inference and learning in nonlinear dynamical systems.
In 12th European Symposium on Artificial Neural Networks ESANN'04,
Brugge, Belgium,
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Machiel Westerdijk,
Henk-Jaap de Walle,
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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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Improved unscented Kalman smoothing for stock volatility estimation.
In A. Bassos,
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Stable fixed points of loopy belief propagation are minima of the Bethe free energy.
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Approximate inference and constrained optimization.
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Generalized belief propagation for approximate inference in hybrid Bayesian networks.
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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.
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Automatic categorization of web pages and user clustering with mixtures of hidden Markov models.
In Osmar Zaïane,
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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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Multi-scale Switching Linear Dynamical Systems.
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Model Clustering for Neural Network Ensembles.
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Expectation propagation for approximate inference in dynamic Bayesian networks.
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Visualization of process data with dynamic Bayesian networks.
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IPF for discrete chain factor graphs.
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Task clustering for learning to learn.
In B. Kröse,
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Probability assessment with maximum entropy in Bayesian networks.
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Survival analysis: a neural-Bayesian approach.
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General bias/variance decomposition with target independent variance of error functions derived from the exponential family of distributions.
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Empirical Bayes for learning to learn.
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EM algorithms for self-organizing maps.
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Bert Kappen,
Martijn Leisink,
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Variational methods for approximate reasoning in graphical models.
In Proceedings of RWC'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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A neural-Bayesian approach to survival analysis.
In Proceedings of ICANN99,
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Model clustering by deterministic annealing.
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Selecting weighting factors in logarithmic opinion pools.
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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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David Barber,
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Probabilistic knowledge representation.
In Proceedings of RWC�98,
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Balancing between bagging and bumping.
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Practical confidence and prediction intervals.
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Practical confidence and prediction intervals for prediction tasks.
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Input selection with partial retraining.
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Tom Heskes and Bert Kappen.
Self-organization and nonparametric regression.
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Confidence values for neural networks.
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Presentation order and on-line learning.
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André Pastoors and Tom Heskes.
Output coding and modularity for multi-class problems.
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A neural model of visual attention.
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Stochastics of on-line backpropagation.
In Proceedings of the European Symposium on Artificial Neural Networks '94,
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Scaling properties of on-line learning with momentum.
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Wim Wiegerinck,
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On-line learning with momentum for nonlinear learning rules.
In M. Marinaro and G. Morasso, editors,
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Guaranteed convergence of learning rules.
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Error potentials for self-organization.
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Global performance of learning rules.
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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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Tom Heskes,
Bert Kappen,
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Neural networks learning in a changing environment.
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Note: Also presented at IJCNN'91, volume 1, pages 823-828.
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