Tom Heskes

Tom Heskes, Wim Wiegerinck, Jan-Joost Spanjers, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Bayesian Incremental Utility Elicitation with Apoplication to Hearing Aids Personalization

Tom Heskes, de B Bert Vries, A. Ypma, 2008 .

Approximate Marginals in Latent Gaussian Models

Tom Heskes, Botond Cseke, 2011, J. Mach. Learn. Res..

A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments

Tom Heskes, Rob Eisinga, Rainer Breitling, 2014, BMC Bioinformatics.

Stability Conditions for L1/Lp Regularization

Tom Heskes, M.A.J. van Gerven, 2008 .

Use of the Noisy Threshold Function in Building Bayesian Networks

Peter J. F. Lucas, Tom Heskes, Rasa Jurgelenaite, 2005, BNAIC.

Sparse Preference Learning

Tom Heskes, Evgeni Tsivtsivadze, 2010, NIPS 2010.

Neighborhood Co-regularized Multi-view Spectral Clustering of Microbiome Data

Tom Heskes, Evgeni Tsivtsivadze, Rita Verhelst, 2013, PSL.

Retrieval of pattern sequences at variable speeds in a neural network with delays

Tom Heskes, Stan C. A. M. Gielen, T. Heskes, 1992, Neural Networks.

Global performance of learning rules

Hilbert J. Kappen, Tom Heskes, 1992 .

Multi-task Preference learning with Gaussian Processes

Tom Heskes, Perry Groot, Adriana Birlutiu, 2009, ESANN.

Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers

Tom Heskes, Ben Pelzer, Rob Eisinga, 2017, BMC Bioinformatics.

A Neural Model of Visual Attention

Tom Heskes, Stan C. A. M. Gielen, Piërre van de Laar, 1995, SNN Symposium on Neural Networks.

General bias/variance decomposition with target independent variance of error functions derived from the exponential family of distributions

Tom Heskes, Jakob Vogdrup Hansen, T. Heskes, 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

Gaussian Quadrature Based Expectation Propagation

Tom Heskes, Onno Zoeter, T. Heskes, 2005, AISTATS.

Exploring the Noisy Threshold Function in Designing Bayesian Networks

Peter J. F. Lucas, Tom Heskes, Rasa Jurgelenaite, 2005, SGAI Conf..

Exploring the impact of alternative feature representations on BCI classification

Tom Heskes, Marcel van Gerven, Ali Bahramisharif, 2009, ESANN.

A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation

Tom Heskes, Bram Platel, Mohsen Ghafoorian, 2016, SPIE Medical Imaging.

Bayesian Sparse Partial Least Squares

Concha Bielza, Pedro Larrañaga, Tom Heskes, 2013, Neural Computation.

Editorial: One Year as EiC, and Editorial-Board Changes at TNN

Michael G. Paulin, Kazushi Ikeda, Rhee Man Kil, 2011, IEEE Trans. Neural Networks.

The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection pdf

Tom Heskes, Wouter Kuijper, Harm Berntsen, 2016, ArXiv.

Kernel Principal Component Ranking: Robust Ranking on Noisy Data

Tom Heskes, Botond Cseke, Evgeni Tsivtsivadze, 2009 .

Overview and Evaluation of Premise Selection Techniques for Large Theory Mathematics

Josef Urban, Tom Heskes, Evgeni Tsivtsivadze, 2012, IJCAR.

Empirical Bayes for Learning to Learn

Tom Heskes, T. Heskes, 2000, ICML.

BCM: toolkit for Bayesian analysis of Computational Models using samplers

Tom Heskes, Tjeerd M. H. Dijkstra, Bram Thijssen, 2016, BMC Systems Biology.

Learning the Causal Structure of Copula Models with Latent Variables

Tom Heskes, Ruifei Cui, Perry Groot, 2018, UAI.

Dynamic decoding of ongoing perception

Tom Heskes, Marcel van Gerven, Floris P. de Lange, 2011, NeuroImage.

Global Optimization of Integrated Objectives using Gaussian Processes

Tom Heskes, Perry Groot, Adriana Birlutiu, 2009 .

Incremental Utility Elicitation for Adaptive Personalization

Tom Heskes, Bert de Vries, T. Heskes, 2005, BNAIC.

Online Co-regularized Algorithms

Tom Heskes, Evgeni Tsivtsivadze, Tom de Ruijter, 2012, Discovery Science.

Semi-supervised Ranking Pursuit pdf

Tom Heskes, Evgeni Tsivtsivadze, 2013, ArXiv.

Learning and approximate inference in dynamic hierarchical models

Tom Heskes, Bart Bakker, T. Heskes, 2007, Comput. Stat. Data Anal..

Improving Cox survival analysis with a neural‐Bayesian approach

Bert Kappen, Tom Heskes, Jan Neijt, 2004, Statistics in medicine.

Approximate Inference and Constrained Optimization

Hilbert J. Kappen, Tom Heskes, Kees Albers, 2002, UAI.

Generalized belief propagation for approximate inference in hybrid Bayesian networks

Tom Heskes, Onno Zoeter, 2003, AISTATS.

Copula PC Algorithm for Causal Discovery from Mixed Data

Tom Heskes, Ruifei Cui, Perry Groot, 2016, ECML/PKDD.

Spectral Ranking of Causal Influence in Complex Systems pdf

Tom Claassen, Tom Heskes, Errol Zalmijn, 2020, Entropy.

On the decoding of intracranial data using sparse orthonormalized partial least squares.

Tom Heskes, Marcel A J van Gerven, Zenas C Chao, 2012, Journal of neural engineering.

Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities

Elena Marchiori, Tom Heskes, Bram van Ginneken, 2016, Scientific Reports.

Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies

Tom Heskes, T. Heskes, 2006, J. Artif. Intell. Res..

Computing Lower and Upper Bounds on the Probability of Causal Statements

Tom Heskes, Tom Claassen, Perry Groot, 2016, Probabilistic Graphical Models.

Sparse orthonormalized partial least squares

Tom Heskes, M.A.J. van Gerven, T. Heskes, 2010 .

Stochastics of on-line backpropagation

Tom Heskes, 1994 .

Small white matter lesion detection in cerebral small vessel disease

Nico Karssemeijer, Elena Marchiori, Tom Heskes, 2015, Medical Imaging.

Gaussian mixture models improve fMRI-based image reconstruction

Tom Heskes, Marcel van Gerven, Sanne Schoenmakers, 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

On the Uniqueness of Loopy Belief Propagation Fixed Points

Tom Heskes, T. Heskes, 2004, Neural Computation.

Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models

Tom Heskes, Alexander Ypma, T. Heskes, 2002, WEBKDD.

Gaussian mixture models and semantic gating improve reconstructions from human brain activity

Tom Heskes, Marcel van Gerven, Umut Güçlü, 2015, Front. Comput. Neurosci..

Improving posterior marginal approximations in latent Gaussian models

Tom Heskes, Botond Cseke, 2010, AISTATS.

A Linear Gaussian Framework for Decoding of Perceived Images

Tom Heskes, Marcel van Gerven, T. Heskes, 2012, 2012 Second International Workshop on Pattern Recognition in NeuroImaging.

Visualization of process data with dynamic Bayesian Networks

Tom Heskes, Onno Zoeter, 2002 .

Bigger Buffer k-d Trees on Multi-Many-Core Systems

Tom Heskes, Fabian Gieseke, Christian Igel, 2018, VECPAR.

Learning causal network structure from multiple (in)dependence models

Tom Heskes, Tom Claassen, 2010 .

Model clustering by deterministic annealing

Tom Heskes, Bart Bakker, 1999, ESANN.

Causal Discovery from Databases with Discrete and Continuous Variables

Tom Heskes, Tom Claassen, Perry Groot, 2014, Probabilistic Graphical Models.

Input selection based on an ensemble

Tom Heskes, Piërre van de Laar, T. Heskes, 2000, Neurocomputing.

Error potentials for self-organization

Hilbert J. Kappen, Tom Heskes, H. Kappen, 1993, IEEE International Conference on Neural Networks.

Survival Analysis: A Neural-Bayesian Approach

Hilbert J. Kappen, Tom Heskes, Bart J. Bakker, 2000, ANNIMAB.

Efficiently learning the preferences of people

Tom Heskes, Perry Groot, Adriana Birlutiu, 2012, Machine Learning.

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies

Tom Heskes, Nathaniel D. Daw, Michael J. Frank, 2018, bioRxiv.

Handwritten digits in fMRI ('69' data set)

Tom Heskes, F. P. de Lange, M.A.J. van Gerven, 2018 .

Quantifying uncertainty in brain network measures using Bayesian connectomics

Tom Heskes, Marcel van Gerven, Max Hinne, 2014, Front. Comput. Neurosci..

Causal Discovery from Big Data - Mission (Im)possible?

Tom Heskes, 2016, ICAART.

Pruning Using Parameter and Neuronal Metrics

Tom Heskes, Piërre van de Laar, T. Heskes, 1999, Neural Computation.

Predicting newspaper sales: JED system 'weathers' the test

Hilbert J. Kappen, Tom Heskes, H. Kappen, 2000 .

A structure independent algorithm for causal discovery

Tom Heskes, Tom Claassen, 2011, ESANN.

A neural-Bayesian approach to survival analysis

Tom Heskes, Bart Bakker, T. Heskes, 1999 .

Bayesian Probabilities for Constraint-Based Causal Discovery

Tom Heskes, Tom Claassen, T. Heskes, 2013, IJCAI.

Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin

Nico Karssemeijer, Elena Marchiori, Tom Heskes, 2016, NeuroImage: Clinical.

Towards hearing-aid personalization: preference elicitation from audiological data

Tom Heskes, Perry Groot, Adriana Birlutiu, 2010 .

Selecting Weighting Factors in Logarithmic Opinion Pools

Tom Heskes, T. Heskes, 1997, NIPS.

Neural network system, JED, offers solution for predicting single-copy sales

Hilbert J. Kappen, Tom Heskes, 1997 .

Predicting carcinoid heart disease with the noisy-threshold classifier

Peter J. F. Lucas, Tom Heskes, Marcel van Gerven, 2007, Artif. Intell. Medicine.

Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation

Nico Karssemeijer, Elena Marchiori, Tom Heskes, 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

Domain Generalization Based on Transfer Component Analysis

Tom Heskes, Thomas Natschläger, Holger Schöner, 2015, IWANN.

Large-Scale Local Causal Inference of Gene Regulatory Relationships

Tom Heskes, Tom Claassen, Ioan Gabriel Bucur, 2019, Int. J. Approx. Reason..

Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs

Tom Heskes, Max Hinne, Marcel A. J. van Gerven, 2015, PloS one.

Novel approximations for inference in nonlinear dynamical systems using expectation propagation

Tom Heskes, Alexander Ypma, T. Heskes, 2005, Neurocomputing.

Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach

Tom Heskes, T. Heskes, 1998, ICML.

Pattern Recognition in Bioinformatics

Elena Marchiori, Tom Heskes, Tjeerd M. H. Dijkstra, 2010 .

Multi-view Multi-class Classification for Identification of Pathogenic Bacterial Strains

Tom Heskes, Evgeni Tsivtsivadze, Armand Paauw, 2013, MCS.

On-line Learning with Momentum for Nonlinear Learning Rules

Tom Heskes, Wim Wiegerinck, Andrzej Komoda, 1994 .

Stochastics of on-line back-propagation

Tom Heskes, 1994, ESANN.

Expectation Propagation

Tom Heskes, T. Heskes, 2010, Encyclopedia of Machine Learning and Data Mining.

Approximate Expectation Maximization

Tom Heskes, Onno Zoeter, Wim Wiegerinck, 2003, NIPS.

Massively-parallel best subset selection for ordinary least-squares regression

Tom Heskes, Fabian Gieseke, Christian Igel, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

The stablespec package for causal discovery on cross-sectional and longitudinal data in R

Tom Heskes, Perry Groot, Ridho Rahmadi, 2018, Neurocomputing.

Guaranteed Convergence of Learning in Neural Networks

Tom Heskes, 1993 .

Transition times in self-organizing maps

Tom Heskes, T. Heskes, 1996, Biological Cybernetics.

Causality on longitudinal data: Stable specification search in constrained structural equation modeling

Tom Heskes, Perry Groot, Ridho Rahmadi, 2015, AALTD@PKDD/ECML.

A comparative study of cell classifiers for image-based high-throughput screening

Tom Heskes, Tjeerd Dijkstra, Syed Abbas, 2014, BMC Bioinformatics.

THE STATISTICAL PROPERTIES OF GENE-SET ANALYSIS FOR GWAS DATA

Tom Heskes, Danielle Posthuma, Benjamin M. Neale, 2017 .

How Dependencies between Successive Examples Affect On-Line Learning

Tom Heskes, Wim Wiegerinck, W. Wiegerinck, 1996, Neural Computation.

Interpreting single trial data using groupwise regularisation

Tom Heskes, Marcel van Gerven, Ole Jensen, 2009, NeuroImage.

Confidence values for neural networks

Hilbert J. Kappen, Tom Heskes, C.C.A.M. Gielen, 1995 .

Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach

Tom Heskes, José Miguel Hernández-Lobato, Tjeerd Dijkstra, 2007, NIPS.

Deterministic approximate inference techniques for conditionally Gaussian state space models

Tom Heskes, Onno Zoeter, T. Heskes, 2006, Stat. Comput..

Speaker diarization using gesture and speech

Peter Wittenburg, Tom Heskes, Marijn Huijbregts, 2014, INTERSPEECH.

Robust Estimation of Gaussian Copula Causal Structure from Mixed Data with Missing Values

Tom Heskes, Ruifei Cui, Perry Groot, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Self-organizing maps, vector quantization, and mixture modeling

Tom Heskes, T. Heskes, 2001, IEEE Trans. Neural Networks.

Approximate algorithms for neural-Bayesian approaches

Hilbert J. Kappen, Tom Heskes, Bart Bakker, 2002, Theor. Comput. Sci..

L1/Lp regularization of Differences

Tom Heskes, M.A.J. van Gerven, 2008 .

MAGMA: Generalized Gene-Set Analysis of GWAS Data

Joris M. Mooij, Tom Heskes, Christiaan A. de Leeuw, 2015, PLoS Comput. Biol..

Task Clustering and Gating for Bayesian Multitask Learning

Tom Heskes, Bart Bakker, T. Heskes, 2003, J. Mach. Learn. Res..

Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD

Tom Claassen, Tom Heskes, Jan K. Buitelaar, 2016, International Journal of Data Science and Analytics.

Multi-output Ranking for Automated Reasoning

Josef Urban, Herman Geuvers, Tom Heskes, 2011, KDIR.

Noisy Threshold Functions for Modelling Causal Independence in Bayesian Networks

Peter J. F. Lucas, Tom Heskes, Rasa Jurgelenaite, 2006 .

Neural Decoding with Hierarchical Generative Models

Tom Heskes, Marcel van Gerven, Floris P. de Lange, 2010, Neural Computation.

Bayesian Monte Carlo for the Global Optimization of Expensive Functions

Tom Heskes, Adriana Birlutiu, Perry Groot, 2010, ECAI.

Stable Specification Search in Structural Equation Models with Latent Variables

Tom Heskes, Perry Groot, Ridho Rahmadi, 2018, ACM Trans. Intell. Syst. Technol..

Learning causal structure from mixed data with missing values using Gaussian copula models

Tom Heskes, Ruifei Cui, Perry Groot, 2018, Statistics and Computing.

A scalable preference model for autonomous decision-making

Tom Heskes, Wolfgang Ketter, Maytal Saar-Tsechansky, 2018, Machine Learning.

Predicting Preference Judgments of Individual Normal and Hearing-Impaired Listeners With Gaussian Processes

James M. Kates, Tom Heskes, Perry Groot, 2011, IEEE Transactions on Audio, Speech, and Language Processing.

A Markov Random Field Approach to Neural Encoding and Decoding

Tom Heskes, Marcel van Gerven, Eric Maris, 2011, ICANN.

Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior

Tom Heskes, Marcel van Gerven, Botond Cseke, 2010, NeuroImage.

Explanation

Subbarao Kambhampati, Krzysztof Krawiec, Moshe Sipper, 1953, Cladistics : the international journal of the Willi Hennig Society.

Evolutionary Computing

Subbarao Kambhampati, Krzysztof Krawiec, Moshe Sipper, 1997, Lecture Notes in Computer Science.

Novel approximations for inference and learning in nonlinear dynamical systems

Tom Heskes, Alexander Ypma, 2004, ESANN.

Practical Confidence and Prediction Intervals

Tom Heskes, T. Heskes, 1996, NIPS.

Structurally-informed Bayesian functional connectivity analysis

Luca Ambrogioni, Tom Heskes, Marcel van Gerven, 2014, NeuroImage.

IPF for Discrete Chain Factor Graphs pdf

Tom Heskes, Wim Wiegerinck, W. Wiegerinck, 2002, UAI.

Causal discovery in multiple models from different experiments

Tom Heskes, Tom Claassen, T. Heskes, 2010, NIPS.

Regularized Output Kernel Regression for protein-protein interaction prediction: application to link transfer and transduction

Tom Heskes, Marie Szafranski, Adriana Birlutiu, 2010, NIPS 2010.

Using Topology Information for Protein-Protein Interaction Prediction

Tom Heskes, Adriana Birlutiu, T. Heskes, 2014, PRIB.

Large Scale Co-Regularized Ranking

Tom Heskes, Evgeni Tsivtsivadze, K. Hofmann, 2012 .

MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models pdf

Tom Claassen, Tom Heskes, Ioan Gabriel Bucur, 2020, UAI.

Unsupervised Feature Learning for Visual Sign Language Identification

Peter Wittenburg, Tom Heskes, Sebastian Drude, 2014, ACL.

Practical confidence and prediction intervals for prediction tasks

Hilbert J. Kappen, Tom Heskes, Wim Wiegerinck, 1997 .

Model Clustering for Neural Network Ensembles

Tom Heskes, Bart Bakker, 2002, ICANN.

Nonlinear perception of hearing-impaired people using preference learning with Gaussian Processes

Tom Heskes, Perry Groot, Tjeerd M. H. Dijkstra, 2008 .

Optimising newspaper sales using neural-Bayesian technology

Tom Heskes, Wim Wiegerinck, Bart Bakker, 2003, Neural Computing & Applications.

The Dynamic Beamformer

Marcel A. J. van Gerven, Tom Heskes, Zoubin Ghahramani, 2011, MLINI.

Personalization of Hearing Aids through Bayesian Preference Elicitation

Tom Heskes, Perry Groot, Adriana Birlutiu, 2010 .

Empirical Bayesian random censoring threshold model improves detection of differentially abundant proteins.

Tom Heskes, Tjeerd M H Dijkstra, Frank Koopmans, 2014, Journal of proteome research.

EM Algorithm for Symmetric Causal Independence Models

Tom Heskes, Rasa Jurgelenaite, T. Heskes, 2006, ECML.

A Logical Characterization of Constraint-Based Causal Discovery

Tom Heskes, Tom Claassen, T. Heskes, 2011, UAI.

KeCo: Kernel-Based Online Co-agreement Algorithm

Tom Heskes, Evgeni Levin, Laurens Wiel, 2015, Discovery Science.

Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data

Tom Heskes, Tom Claassen, Perry Groot, 2015, AIME.

RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets

Tom Heskes, Rob Eisinga, Rainer Breitling, 2017, Bioinform..

Probability assessment with maximum entropy in Bayesian networks

Tom Heskes, Wim Wiegerinck, 2001 .

Change Point Problems in Linear Dynamical Systems

Tom Heskes, Onno Zoeter, T. Heskes, 2005, J. Mach. Learn. Res..

Improving Native Language Identification with TF-IDF Weighting

Peter Wittenburg, Tom Heskes, Marcos Zampieri, 2013, BEA@NAACL-HLT.

Expectation Propagation for Rating Players in Sports Competitions

Tom Heskes, Adriana Birlutiu, T. Heskes, 2007, PKDD.

Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates

Tom Heskes, Lodewyk F. A. Wessels, Tjeerd Dijkstra, 2017, Bioinform..

Multi-Domain Transfer Component Analysis for Domain Generalization

Tom Heskes, Thomas Natschläger, Holger Schöner, 2017, Neural Processing Letters.

Co-Regularized Least-Squares for Label Ranking

Tapio Salakoski, Tapio Pahikkala, Tom Heskes, 2010, Preference Learning.

Learning of causal relations

Michael Biehl, Joris M. Mooij, Tom Heskes, 2011, ESANN.

The Use of Being Stubborn and Introspective

Tom Heskes, 2000 .

Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates

Tom Heskes, Marcel van Gerven, Max Hinne, 2015, PLoS Comput. Biol..

Motion history images for online speaker/signer diarization

Peter Wittenburg, Tom Heskes, Sebastian Drude, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Mutual Information Estimation with Random Forests

Tom Heskes, Mike Koeman, 2014, ICONIP.

Handwritten characters in fMRI ('BRAINS' data set)

Tom Heskes, Markus Barth, M.A.J. van Gerven, 2018 .

The gesturer is the speaker

Peter Wittenburg, Tom Heskes, Binyam Gebrekidan Gebre, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

A theoretical comparison of batch-mode, on-line, cyclic, and almost-cyclic learning

Tom Heskes, Wim Wiegerinck, 1996, IEEE Trans. Neural Networks.

Linear reconstruction of perceived images from human brain activity

Tom Heskes, Marcel van Gerven, Markus Barth, 2013, NeuroImage.

A Bayesian Approach to Constraint Based Causal Inference

Tom Heskes, Tom Claassen, T. Heskes, 2012, UAI.

Bias/Variance Decompositions for Likelihood-Based Estimators

Tom Heskes, T. Heskes, 1998, Neural Computation.

Input Selection with Partial Retraining

Tom Heskes, Stan C. A. M. Gielen, Piërre van de Laar, 1997, ICANN.

Exploring Constraint: Simulating Self-Organization and Autogenesis in the Autogenic Automaton

Tom Heskes, Stefan Leijnen, Terrence W. Deacon, 2016, ALIFE.

Bayesian Modeling of Brain Networks

Tom Heskes, M.A.J. van Gerven, 2013 .

Hidden Markov Models for Reading Words from the Human Brain

Tom Heskes, Marcel van Gerven, Sanne Schoenmakers, 2015, 2015 International Workshop on Pattern Recognition in NeuroImaging.

Expectation Propagation

Tom Heskes, 2017, Encyclopedia of Machine Learning and Data Mining.

Monitoring non-linear and switching dynamical systems

Tom Heskes, C.C.A.M. Gielen, O. R. Zoeter, 2000 .

Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness pdf

Tom Heskes, Tom Claassen, Ioan Gabriel Bucur, 2017, AISTATS.

Learning2Reason

Josef Urban, Herman Geuvers, Tom Heskes, 2011, Calculemus/MKM.

Symmetric Causal Independence Models for Classification

Tom Heskes, Rasa Jurgelenaite, T. Heskes, 2006, Probabilistic Graphical Models.

Bayesian brain-computer interfacing interpretation of patient intentions from single-trial EEG

Tom Heskes, M.A.J. van Gerven, Ole Nørregaard Jensen, 2006 .

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models pdf

Tom Claassen, Tom Heskes, Ioan Gabriel Bucur, 2020, NeurIPS.

On Causal Discovery with Cyclic Additive Noise Models

Bernhard Schölkopf, Joris M. Mooij, Tom Heskes, 2011, NIPS.

Covert Attention as a Paradigm for Subject-Independent Brain-Computer Interfacing

Tom Heskes, Matthias S. Treder, Ali Bahramisharif, 2011, MLINI.

Fractional Belief Propagation

Tom Heskes, Wim Wiegerinck, W. Wiegerinck, 2002, NIPS.

Clustering ensembles of neural network models

Tom Heskes, Bart Bakker, T. Heskes, 2003, Neural Networks.

Hierarchical Visualization of Time-Series Data Using Switching Linear Dynamical Systems

Tom Heskes, Onno Zoeter, T. Heskes, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering

Tapio Pahikkala, Tom Heskes, Fabian Gieseke, 2015, IDA.

Multi-task preference learning with an application to hearing aid personalization

Tom Heskes, Adriana Birlutiu, Perry Groot, 2010, Neurocomputing.

Partial Retraining: A New Approach to Input Relevance Determination

Tom Heskes, Stan C. A. M. Gielen, Piërre van de Laar, 1999, Int. J. Neural Syst..

Semantic Graph Kernels for Automated Reasoning

Josef Urban, Herman Geuvers, Tom Heskes, 2011, SDM.

Bayesian inference of structural brain networks

Tom Heskes, Marcel van Gerven, Max Hinne, 2013, NeuroImage.

On-line learning with time-correlated examples

Tom Heskes, Wim Wiegerinck, 1998 .

Gene regulation in the intraerythrocytic cycle of Plasmodium falciparum

Tom Heskes, Tjeerd Dijkstra, Rasa Jurgelenaite, 2009, Bioinform..

Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy

Tom Heskes, T. Heskes, 2002, NIPS.

Automatic sign language identification

Peter Wittenburg, Tom Heskes, Binyam Gebrekidan Gebre, 2013, 2013 IEEE International Conference on Image Processing.

On-line learning processes in artificial neural networks

Hilbert J. Kappen, Tom Heskes, H. Kappen, 1993 .

Causality on cross-sectional data: Stable specification search in constrained structural equation modeling pdf

Tom Heskes, Perry Groot, Ridho Rahmadi, 2015, Appl. Soft Comput..

A novel Bayesian approach for latent variable modeling from mixed data with missing values

Tom Heskes, Ruifei Cui, Perry Groot, 2018, Stat. Comput..

Fault Trees, Decision Trees, And Binary Decision Diagrams: A Systematic Comparison

Tom Heskes, Mariëlle Stoelinga, L. A. Jiménez-Roa, 2021, Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021).

Going Grayscale: The Road to Understanding and Improving Unlearnable Examples

Martha Larson, Tom Heskes, Twan van Laarhoven, 2021, ArXiv.

Spectral Ranking of Causal Influence in Complex Systems

Tom Heskes, Tom Claassen, Errol Zalmijn, 2021, Entropy.

Swift sky localization of gravitational waves using deep learning seeded importance sampling pdf

Tom Heskes, Twan van Laarhoven, Sarah Caudill, 2021, ArXiv.

Non-parametric synergy modeling of chemical compounds with Gaussian processes

Tom Heskes, Tjeerd Dijkstra, Yuliya Shapovalova, 2022, BMC Bioinform..

How to Evaluate Uncertainty Estimates in Machine Learning for Regression? pdf

Tom Heskes, Eric Cator, Laurens Sluijterman, 2021, ArXiv.

Learning Equational Theorem Proving pdf

Josef Urban, Tom Heskes, Mikol'avs Janota, 2021, ArXiv.

MAGMA: Generalized Gene-Set Analysis of GWAS Data

Joris M. Mooij, Tom Heskes, Danielle Posthuma, 2015, PLoS Comput. Biol..

Convolutional neural networks for transient candidate vetting in large-scale surveys

Brad E. Tucker, Tom Heskes, Fabian Gieseke, 2017, 1708.08947.

Arrowhead completeness from minimal conditional independencies

Tom Heskes, Tom Claassen, T. Heskes, 2010 .

Stable Specification Searches in Structural Equation Modeling Using a Multi-objective Evolutionary Algorithm

Tom Heskes, Perry Groot, Ridho Rahmadi, 2014 .

Discussion of ``Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations'' by H. Rue, S. Martino and N. Chopin

Tom Heskes, Botond Cseke, T. Heskes, 2009 .

Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions

Marcel A. J. van Gerven, Arno Solin, Tom Heskes, 2016, 1604.04931.

Self-organization and nonparametric regression

Hilbert J. Kappen, Tom Heskes, 1995 .

Learning symmetric causal independence models

Tom Heskes, Rasa Jurgelenaite, T. Heskes, 2008, Machine Learning.

Lateralized responses during covert attention are modulated by target eccentricity

Marcel A. J. van Gerven, Tom Heskes, Ole Jensen, 2011, Neuroscience Letters.

The exact probability distribution of the rank product statistics for replicated experiments

Tom Heskes, Rainer Breitling, Rob Eisinga, 2013, FEBS letters.

Expectation propagation and generalised EP methods for inference in switching linear dynamical systems

Tom Heskes, Onno Zoeter, T. Heskes, 2011 .

Using symmetric causal independence models to predict gene expression from sequence data

Tom Heskes, Tmh Tjeerd Dijkstra, Rasa Jurgelenaite, 2007 .

A Bayesian psychophysical model for angular variables

Tom Heskes, Onno Zoeter, Tjeerd M. H. Dijkstra, 2013 .

Statistical Evidence Suggests that Inattention Drives Hyperactivity/Impulsivity in Attention Deficit-Hyperactivity Disorder

Tom Heskes, Tom Claassen, Perry Groot, 2016, PloS one.

A Causal and Mediation Analysis of the Comorbidity Between Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD)

Tom Heskes, Tom Claassen, Perry Groot, 2017, Journal of autism and developmental disorders.