T. Heskes

T. Heskes, B. Franke, J. Buitelaar, 2015, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

Hearclip : an application of Bayesian machine learning to personalization of hearing aids

Tom Heskes, Adriana Birlutiu, Seyed Iman Mossavat, 2010 .

Prediction of bladder outlet obstruction in men with lower urinary tract symptoms using artificial neural networks.

J J de la Rosette, T. Heskes, L. Kiemeney, 2000, The Journal of urology.

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

Max A. Little, Yordan P. Raykov, T. Heskes, 2020, Journal of medical Internet research.

BNAIC'03: Proceedings of the 15th Belgium-Netherlands Artificial Intelligence Conference

Tom Heskes, Louis Vuurpijl, Peter J. F. Lucas, 2003 .

Explanation

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

Pattern Recognition in Bioinformatics

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

Cognitive behavioural therapy with optional graded exercise therapy in patients with severe fatigue with myotonic dystrophy type 1: a multicentre, single-blind, randomised trial

T. Heskes, P. Donnan, J. Deux, 2018, The Lancet Neurology.

Semi-automated Rasch analysis with differential item functioning.

Ioan Gabriel Bucur, T. Heskes, P. Groot, 2022, Behavior research methods.

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

T. Heskes, G. Baltus, S. Caudill, 2021, Physical Review D.

Machine Learning Meets The Herbrand Universe

T. Heskes, J. Urban, Miroslav Olvs'ak, 2022, 2210.03590.

Im proving posterior m arginal approxim ations in latent

T. Heskes, Botond Cseke, aussian m odels, 2017 .

THE STATISTICAL PROPERTIES OF GENE-SET ANALYSIS FOR GWAS DATA

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

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

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

autoRasch: An R Package to Do Semi-Automated Rasch Analysis

Ioan Gabriel Bucur, T. Heskes, P. Groot, 2022, Applied psychological measurement.

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

Ioan Gabriel Bucur, T. Heskes, Ruifei Cui, 2019, Statistics and Computing.

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

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

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

T. Heskes, R. Eisinga, M. Grotenhuis, 2017, BMC Bioinformatics.

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

T. Heskes, Ruifei Cui, P. Groot, 2018, Statistics and Computing.

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 .

Composite Survival Index to Compare Virulence Changes in Azole-Resistant Aspergillus fumigatus Clinical Isolates

T. Heskes, W. Melchers, M. Arendrup, 2013, PloS one.

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

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

Transition times in self-organizing maps

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

Efficiently learning the preferences of people

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

Efficient sampling of Gaussian graphical models using conditional Bayes factors

Marcel van Gerven, Max Hinne, Tom Heskes, 2014, 1409.2676.

Neural Decoding with Hierarchical Generative Models

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

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

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

Sparse Approximate Inference for Spatio-Temporal Point Process Models

Botond Cseke, Andrew Zammit-Mangion, Tom Heskes, 2013, 1305.4152.

A scalable preference model for autonomous decision-making

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

Deterministic approximate inference techniques for conditionally Gaussian state space models

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

Additive Dose Response Models: Explicit Formulation and the Loewe Additivity Consistency Condition

T. Heskes, T. Dijkstra, Simone Lederer, 2017, bioRxiv.

MAGMA: Generalized Gene-Set Analysis of GWAS Data

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

Learning the Causal Structure of Copula Models with Latent Variables

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

MAGMA: Generalized Gene-Set Analysis of GWAS Data

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

Structurally-informed Bayesian functional connectivity analysis

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

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

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

Quantitative Genetic Scoring, or how to put a number on an arbitrary genetic region

T. Heskes, B. Franke, A. Børglum, 2020, bioRxiv.

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

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

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.

Memantine treatment does not affect compulsive behavior or frontostriatal connectivity in an adolescent rat model for quinpirole-induced compulsive checking behavior

T. Heskes, B. Franke, D. Joel, 2022, Psychopharmacology.

Evaluation Data

Joshua D. Knowles, S. Kambhampati, T. Heskes, 2010, Encyclopedia of Machine Learning.

Genetic background of grey matter brain networks related to attention-deficit/hyperactivity disorder

T. Heskes, B. Franke, J. Turner, 2022, medRxiv.

Bayesian Sparse Partial Least Squares

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

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

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

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

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

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.

Computing Lower and Upper Bounds on the Probability of Causal Statements

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

Causal Discovery from Databases with Discrete and Continuous Variables

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

Learning Sparse Causal Models is not NP-hard pdf

Tom Claassen, Joris M. Mooij, Tom Heskes, 2013, UAI.

Bayesian Probabilities for Constraint-Based Causal Discovery

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

UvA-DARE (Digital Academic Repository) Supplement - Learning Sparse Causal Models is not NP-hard

T. Heskes, J. Mooij, T. Claassen, 2013 .

A Bayesian Approach to Constraint Based Causal Inference

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

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

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

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

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

Non-parametric synergy modeling of chemical compounds with Gaussian processes

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

Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs

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

MSH3 modifies somatic instability and disease severity in Huntington’s and myotonic dystrophy type 1

Nick C Fox, T. Heskes, C. Kennard, 2019, Brain : a journal of neurology.

Task-Dependent Learning of Attention

PIERRE VAN DE LAAR, TOM HESKES, STAN GIELEN, 1997, Neural Networks.

Bayesian Monte Carlo for the Global Optimization of Expensive Functions

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

Understanding the assumptions underlying Mendelian randomization

Ioan Gabriel Bucur, T. Heskes, D. Posthuma, 2021, European Journal of Human Genetics.

Input selection based on an ensemble

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

Semi‐automated Rasch analysis using in‐plus‐out‐of‐questionnaire log likelihood

Perry Groot, Feri Wijayanto, Tom Heskes, 2020, The British journal of mathematical and statistical psychology.

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

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

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.

Methodology of the DCCSS later fatigue study: a model to investigate chronic fatigue in long-term survivors of childhood cancer

T. Heskes, J. Rosmalen, E. Bronkhorst, 2021, BMC Medical Research Methodology.

A Bayesian psychophysical model for angular variables

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

Molecular Machines in the Synapse: Overlapping Protein Sets Control Distinct Steps in Neurosecretion

Tom Heskes, Evgeni Tsivtsivadze, L. Niels Cornelisse, 2012, PLoS Comput. Biol..

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.

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

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

Interpreting single trial data using groupwise regularisation

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

Learning and approximate inference in dynamic hierarchical models

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

Change Point Problems in Linear Dynamical Systems

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

Constraining the parameters of high-dimensional models with active learning pdf

Tom Heskes, Sascha Caron, Bob Stienen, 2019, The European Physical Journal C.

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.

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

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

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).

Evolutionary Computing

Thomas E. Potok, Joshua D. Knowles, Robert M. Patton, 2019, Encyclopedia of Machine Learning and Data Mining.

Self-organizing maps, vector quantization, and mixture modeling

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

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

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

Optimising newspaper sales using neural-Bayesian technology

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

Measuring Parkinson's disease over time: The real‐world within‐subject reliability of the MDS‐UPDRS

Bastiaan R Bloem, Marjan J Meinders, T. Heskes, 2019, Movement disorders : official journal of the Movement Disorder Society.

Personalized monitoring of ambulatory function with a smartphone 2-minute walk test in multiple sclerosis

Ioan Gabriel Bucur, T. Heskes, B. Uitdehaag, 2023, Multiple sclerosis.

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

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

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

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

Linear reconstruction of perceived images from human brain activity

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

Predicting carcinoid heart disease with the noisy-threshold classifier

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

EM Algorithm for Symmetric Causal Independence Models

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

Symmetric Causal Independence Models for Classification

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

Noisy Threshold Functions for Modelling Causal Independence in Bayesian Networks

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

On the Uniqueness of Loopy Belief Propagation Fixed Points

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

Selecting features for BCI control based on a covert spatial attention paradigm

Tom Heskes, Marcel van Gerven, Ole Jensen, 2009, Neural Networks.

Going Grayscale: The Road to Understanding and Improving Unlearnable Examples

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

Bayesian inference of structural brain networks

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

A Logical Characterization of Constraint-Based Causal Discovery

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

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

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

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.

Expectation Propagation for Rating Players in Sports Competitions

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

Incremental Utility Elicitation for Adaptive Personalization

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

Learning from Multiple Annotators with Gaussian Processes

Tom Heskes, Perry Groot, Adriana Birlutiu, 2011, ICANN.

Quantifying uncertainty in brain network measures using Bayesian connectomics

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

Gene regulation in the intraerythrocytic cycle of Plasmodium falciparum

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

Variational methods for approximate reasoning in graphical models

David Barber, Tom Heskes, Hilbert J. Kappen, 2000 .

Semantic Graph Kernels for Automated Reasoning

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

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

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

Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life

Max A. Little, Kasper Claes, Yordan P. Raykov, 2020, IEEE Journal of Biomedical and Health Informatics.

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

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

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study (Preprint)

Max A. Little, Yordan P. Raykov, T. Heskes, 2020 .

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Jesse Alama, Josef Urban, Evgeni Tsivtsivadze, 2011, Journal of Automated Reasoning.

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

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

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

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

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

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

Gaussian mixture models improve fMRI-based image reconstruction

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

Discovering Ecological Relationships in Flowing Freshwater Ecosystems

T. Heskes, M. Huijbregts, L. Posthuma, 2022, Frontiers in Ecology and Evolution.

Overview and Evaluation of Premise Selection Techniques for Large Theory Mathematics

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

Specialized Versus Generic Allied Health Therapy and the Risk of Parkinson's Disease Complications

T. Heskes, Y. Ben-Shlomo, J. Nonnekes, 2022, Movement disorders : official journal of the Movement Disorder Society.

The gesturer is the speaker

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

The effect of cardiovascular risk on disease progression in de novo Parkinson's disease patients: An observational analysis

T. Heskes, B. Bloem, E. Richard, 2023, Frontiers in Neurology.

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.

Bayesian Inference of Whole-Brain Networks

M. Hinne, T. Heskes, M. A. J. van Gerven, 2012, 1202.1696.

Towards individual monitoring of cognition in multiple sclerosis in the digital era: a one-year cohort study (Preprint)

Ioan Gabriel Bucur, T. Heskes, B. Uitdehaag, 2021 .

Improving Cox survival analysis with a neural‐Bayesian approach

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

Approximate algorithms for neural-Bayesian approaches

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

A p proxim ate A lgorith m s for N eural-B ayesian A pproaches.

Bert Kappen, Tom Heskes, Bart Bakker, 2000 .

Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates

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

Fast Laplace Approximation for Gaussian Processes with a Tensor Product Kernel

Tom Heskes, Wolfgang Ketter, Markus Peters, 2014 .

A direct comparison of visual discrimination of shape and size on a large range of aspect ratios

T. Heskes, T. Dijkstra, Syed Saiden Abbas, 2013, Vision Research.

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

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

Speaker diarization using gesture and speech

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

Potential mechanisms of the fatigue‐reducing effect of cognitive‐behavioral therapy in cancer survivors: Three randomized controlled trials

T. Heskes, P. Groot, H. Knoop, 2021, Psycho-oncology.

Stable Specification Search in Structural Equation Models with Latent Variables

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

Domain Generalization Based on Transfer Component Analysis

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

Dynamic decoding of ongoing perception

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

Learning of causal relations

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

A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions

Florence d'Alché-Buc, Tom Heskes, Adriana Birlutiu, 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Unsupervised Feature Learning for Visual Sign Language Identification

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

Automatic sign language identification

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

Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms

T. Heskes, M. Stoelinga, T. Tinga, 2022, IEEE Transactions on Dependable and Secure Computing.

Cyclic Causal Discovery from Continuous Equilibrium Data

P. Smyth, T. Heskes, J. Mooij, 2013, UAI.

KeCo: Kernel-Based Online Co-agreement Algorithm

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

Online Co-regularized Algorithms

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

Large Scale Co-Regularized Ranking

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

Learning Equational Theorem Proving pdf

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

Clustering ensembles of neural network models

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

EM algorithms for self-organizing maps

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.

Improving Native Language Identification with TF-IDF Weighting

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

How to Evaluate Uncertainty Estimates in Machine Learning for Regression?

T. Heskes, E. Cator, Laurens Sluijterman, 2021, ArXiv.

Using Topology Information for Protein-Protein Interaction Prediction

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

Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates

T. Heskes, M. Hinne, R. Janssen, 2015, PLoS Comput. Biol..

Copula PC Algorithm for Causal Discovery from Mixed Data

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

Exploring the impact of alternative feature representations on BCI classification

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

Learning Sparse Causal Models is not NP-hard

T. Heskes, T. Claassen, A. Nicholson, 2013, UAI.

Explanation

Shiau Hong Lim, S. Kambhampati, T. Heskes, 1953, Cladistics : the international journal of the Willi Hennig Society.

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

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

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

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

Likelihood-ratio-based confidence intervals for neural networks

T. Heskes, E. Cator, Laurens Sluijterman, 2023, ArXiv.

Sparse orthonormalized partial least squares

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

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

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

Learning processes in neural networks.

Heskes, Kappen, T. Heskes, 1991, Physical review. A, Atomic, molecular, and optical physics.

Error potentials for self-organization

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

Quantifying uncertainty in brain network measures using Bayesian connectomics

T. Heskes, M. Hinne, R. Janssen, 2014, Front. Comput. Neurosci..

Role of conduct problems in the relation between Attention-Deficit Hyperactivity disorder, substance use, and gaming

T. Heskes, B. Franke, J. Buitelaar, 2020, European Neuropsychopharmacology.

Dynamic decoding of ongoing perception

T. Heskes, P. Kok, M. Gerven, 2011, NeuroImage.

Novel approximations for inference in nonlinear dynamical systems using expectation propagation

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

Large-Scale Local Causal Inference of Gene Regulatory Relationships

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

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

T. Heskes, Botond Cseke, M. Gerven, 2010, NeuroImage.

Optimal Training of Mean Variance Estimation Neural Networks

T. Heskes, E. Cator, Laurens Sluijterman, 2023, ArXiv.

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.

Spectral Ranking of Causal Influence in Complex Systems pdf

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

IMPROVED UNSCENTED K ALM AN SMOOTHING FOR STOCK VOLATILITY ES TI MAT IO N

T. Heskes, O. Zoeter, A. Ypma, 2004 .

Unsupervised anomaly detection algorithms on real-world data: how many do we need?

T. Heskes, Z. Bukhsh, Roel Bouman, 2023, ArXiv.

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).

Automatic Signer Diarization - The Mover Is the Signer Approach

Peter Wittenburg, Tom Heskes, Binyam Gebrekidan Gebre, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Multi-task Preference learning with Gaussian Processes

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

Selecting features for BCI control based on a covert spatial attention paradigm

T. Heskes, Ali Bahramisharif, O. Jensen, 2009, Neural Networks.

Expectation Propagation

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

ON THE DEVELOPMENT OF A BRAIN-COMPUTER INTERFACE SYSTEM USING HIGH-DENSITY MAGNETOENCEPHALOGRAM SIGNALS FOR REAL-TIME CONTROL OF A ROBOT ARM

Robert Oostenveld, Tom Heskes, Ole Jensen, 2007 .

Bayesian Source Localization with the Multivariate Laplace Prior

Tom Heskes, Marcel van Gerven, Botond Cseke, 2009, NIPS.

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).

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

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

Causal discovery in multiple models from different experiments

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

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

Tom Heskes, Perry Groot, Ridho Rahmadi, 2014 .

The statistical properties of gene-set analysis

T. Heskes, B. Neale, D. Posthuma, 2016, Nature reviews genetics.

Approximate Expectation Maximization

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