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ORCIDTom Heskes
Tom M. Heskes
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- affiliation: Radboud University Nijmegen, NL
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2020 – today
- 2024
[j69]Laurens Sluijterman
, Eric Cator, Tom Heskes:
Optimal training of Mean Variance Estimation neural networks. Neurocomputing 597: 127929 (2024)- [j68]Roel Bouman, Zaharah Bukhsh, Tom Heskes:
Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need? J. Mach. Learn. Res. 25: 105:1-105:34 (2024) - [j67]Laurens Sluijterman, Eric Cator, Tom Heskes:
How to evaluate uncertainty estimates in machine learning for regression? Neural Networks 173: 106203 (2024) - [i42]Binyam Gebre, Karoliina Ranta, Stef van den Elzen, Ernst Kuiper, Thijs Baars, Tom Heskes:
Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities. CoRR abs/2402.16073 (2024) - [i41]Roel Bouman, Linda Schmeitz, Luco Buise, Jacco Heres, Yuliya Shapovalova, Tom Heskes:
Acquiring Better Load Estimates by Combining Anomaly and Change-point Detection in Power Grid Time-series Measurements. CoRR abs/2405.16164 (2024) - [i40]Laurens Sluijterman, Frank Kreuwel, Eric Cator, Tom Heskes:
Composite Quantile Regression With XGBoost Using the Novel Arctan Pinball Loss. CoRR abs/2406.02293 (2024) - 2023
- [j66]Lisandro Arturo Jimenez-Roa, Tom Heskes, Tiedo Tinga, Mariëlle Stoelinga:
Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms. IEEE Trans. Dependable Secur. Comput. 20(4): 3317-3327 (2023) - [c97]Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen:
Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. CLeaR 2023: 707-725 - [i39]Laurens Sluijterman, Eric Cator, Tom Heskes:
Optimal Training of Mean Variance Estimation Neural Networks. CoRR abs/2302.08875 (2023) - [i38]Roel Bouman, Zaharah Bukhsh, Tom Heskes:
Unsupervised anomaly detection algorithms on real-world data: how many do we need? CoRR abs/2305.00735 (2023) - [i37]Laurens Sluijterman, Eric Cator, Tom Heskes:
Likelihood-ratio-based confidence intervals for neural networks. CoRR abs/2308.02221 (2023) - [i36]Charlotte Cambier van Nooten, Tom van de Poll, Sonja Füllhase, Jacco Heres, Tom Heskes, Yuliya Shapovalova:
Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid. CoRR abs/2310.01181 (2023) - [i35]Lisandro Arturo Jimenez-Roa, Tom Heskes, Mariëlle Stoelinga:
Fault Trees, Decision Trees, And Binary Decision Diagrams: A Systematic Comparison. CoRR abs/2310.04448 (2023) - 2022
- [j65]Yuliya Shapovalova, Tom Heskes, Tjeerd Dijkstra:
Non-parametric synergy modeling of chemical compounds with Gaussian processes. BMC Bioinform. 23(1): 14 (2022) - [c96]Jelle Piepenbrock, Tom Heskes, Mikolás Janota, Josef Urban:
Guiding an Automated Theorem Prover with Neural Rewriting. IJCAR 2022: 597-617 - [i34]Laurens Sluijterman, Eric Cator, Tom Heskes:
Confident Neural Network Regression with Bootstrapped Deep Ensembles. CoRR abs/2202.10903 (2022) - [i33]Lisandro Arturo Jimenez-Roa, Tom Heskes, Tiedo Tinga, Mariëlle Stoelinga:
Automatic inference of fault tree models via multi-objective evolutionary algorithms. CoRR abs/2204.03743 (2022) - [i32]Jelle Piepenbrock, Josef Urban, Konstantin Korovin, Miroslav Olsák, Tom Heskes, Mikolas Janota:
Machine Learning Meets The Herbrand Universe. CoRR abs/2210.03590 (2022) - 2021
- [j64]Errol Zalmijn, Tom Heskes, Tom Claassen:
Spectral Ranking of Causal Influence in Complex Systems. Entropy 23(3): 369 (2021) - [j63]Yordan P. Raykov, Luc J. W. Evers, Reham Badawy, Bastiaan R. Bloem, Tom M. Heskes, Marjan J. Meinders, Kasper Claes, Max A. Little:
Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life. IEEE J. Biomed. Health Informatics 25(6): 2293-2304 (2021) - [i31]Jelle Piepenbrock, Tom Heskes, Mikolás Janota, Josef Urban:
Learning Equational Theorem Proving. CoRR abs/2102.05547 (2021) - [i30]Laurens Sluijterman, Eric Cator, Tom Heskes:
How to Evaluate Uncertainty Estimates in Machine Learning for Regression? CoRR abs/2106.03395 (2021) - [i29]Alex Kolmus, Grégory Baltus, Justin Janquart, Twan van Laarhoven, Sarah Caudill, Tom Heskes:
Swift sky localization of gravitational waves using deep learning seeded importance sampling. CoRR abs/2111.00833 (2021) - [i28]Zhuoran Liu, Zhengyu Zhao, Alex Kolmus, Tijn Berns, Twan van Laarhoven, Tom Heskes, Martha A. Larson:
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples. CoRR abs/2111.13244 (2021) - 2020
- [c95]Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen:
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. NeurIPS 2020 - [c94]Konrad P. Mielke, Tom Claassen, Mark A. J. Huijbregts, Aafke M. Schipper, Tom M. Heskes:
Discovering cause-effect relationships in spatial systems with a known direction based on observational data. PGM 2020: 305-316 - [c93]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. UAI 2020: 1049-1058 - [i27]Yordan P. Raykov, Luc J. W. Evers, Reham Badawy, Bastiaan R. Bloem, Tom M. Heskes, Marjan J. Meinders, Kasper Claes, Max A. Little:
Probabilistic modelling of gait for robust passive monitoring in daily life. CoRR abs/2004.03047 (2020) - [i26]Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen:
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. CoRR abs/2011.01625 (2020) - [i25]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. CoRR abs/2012.10141 (2020) - [i24]Errol Zalmijn, Tom Heskes, Tom Claassen:
Spectral Ranking of Causal Influence in Complex Systems. CoRR abs/2012.13195 (2020)
2010 – 2019
- 2019
- [j62]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Large-scale local causal inference of gene regulatory relationships. Int. J. Approx. Reason. 115: 50-68 (2019) - [j61]Payam Piray, Amir Dezfouli, Tom Heskes, Michael J. Frank, Nathaniel D. Daw:
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Comput. Biol. 15(6) (2019) - [j60]Ruifei Cui, Perry Groot, Tom Heskes:
Learning causal structure from mixed data with missing values using Gaussian copula models. Stat. Comput. 29(2): 311-333 (2019) - [j59]Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes:
A novel Bayesian approach for latent variable modeling from mixed data with missing values. Stat. Comput. 29(5): 977-993 (2019) - [j58]Ridho Rahmadi, Perry Groot, Tom Heskes:
Stable Specification Search in Structural Equation Models with Latent Variables. ACM Trans. Intell. Syst. Technol. 10(5): 48:1-48:23 (2019) - [i23]Sascha Caron, Tom Heskes, Sydney Otten, Bob Stienen:
Constraining the Parameters of High-Dimensional Models with Active Learning. CoRR abs/1905.08628 (2019) - [i22]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Large-Scale Local Causal Inference of Gene Regulatory Relationships. CoRR abs/1909.03818 (2019) - 2018
- [j57]Bram Thijssen, Tjeerd Dijkstra, Tom Heskes, Lodewyk F. A. Wessels:
Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates. Bioinform. 34(5): 803-811 (2018) - [j56]Ridho Rahmadi, Perry Groot, Tom Heskes:
The stablespec package for causal discovery on cross-sectional and longitudinal data in R. Neurocomputing 275: 2440-2443 (2018) - [j55]Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes:
A scalable preference model for autonomous decision-making. Mach. Learn. 107(6): 1039-1068 (2018) - [c92]Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes:
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. PGM 2018: 37-48 - [c91]Ruifei Cui, Perry Groot, Moritz Schauer, Tom Heskes:
Learning the Causal Structure of Copula Models with Latent Variables. UAI 2018: 188-197 - [c90]Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes:
Bigger Buffer k-d Trees on Multi-Many-Core Systems. VECPAR 2018: 202-214 - [i21]Ridho Rahmadi, Perry Groot, Tom Heskes:
Stable specification search in structural equation model with latent variables. CoRR abs/1805.09527 (2018) - [i20]Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes:
A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values. CoRR abs/1806.04610 (2018) - [i19]Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes:
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. CoRR abs/1809.06827 (2018) - 2017
- [j54]Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes:
Causality on cross-sectional data: Stable specification search in constrained structural equation modeling. Appl. Soft Comput. 52: 687-698 (2017) - [j53]Francesco Del Carratore, Andris Jankevics, Rob Eisinga, Tom Heskes, Fangxin Hong, Rainer Breitling:
RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets. Bioinform. 33(17): 2774-2775 (2017) - [j52]Rob Eisinga, Tom Heskes, Ben Pelzer, Manfred te Grotenhuis:
Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers. BMC Bioinform. 18(1): 68:1-68:18 (2017) - [j51]Elena Sokolova, Daniel von Rhein, Jilly Naaijen, Perry Groot, Tom Claassen, Jan K. Buitelaar, Tom Heskes:
Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD. Int. J. Data Sci. Anal. 3(2): 105-119 (2017) - [j50]Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, Tom Heskes:
Multi-Domain Transfer Component Analysis for Domain Generalization. Neural Process. Lett. 46(3): 845-855 (2017) - [c89]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. AISTATS 2017: 1523-1531 - [c88]Ruifei Cui, Perry Groot, Tom Heskes:
Robust Estimation of Gaussian Copula Causal Structure from Mixed Data with Missing Values. ICDM 2017: 835-840 - [c87]Fabian Gieseke, Kai Lars Polsterer, Ashish Mahabal, Christian Igel, Tom Heskes:
Massively-parallel best subset selection for ordinary least-squares regression. SSCI 2017: 1-8 - [r2]Tom Heskes:
Expectation Propagation. Encyclopedia of Machine Learning and Data Mining 2017: 482-487 - [i18]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. CoRR abs/1704.01864 (2017) - 2016
- [j49]Bram Thijssen, T. M. H. Dijkstra, Tom Heskes, Lodewyk F. A. Wessels:
BCM: toolkit for Bayesian analysis of Computational Models using samplers. BMC Syst. Biol. 10: 100 (2016) - [c86]Terrence W. Deacon, Tom Heskes, Stefan Leijnen:
Exploring Constraint: Simulating Self-Organization and Autogenesis in the Autogenic Automaton. ALIFE 2016: 68-75 - [c85]Tom Heskes:
Causal Discovery from Big Data - Mission (Im)possible?. ICAART (1) 2016: 7 - [c84]Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, I. W. M. van Uden, Frank-Erik de Leeuw, Elena Marchiori, Bram van Ginneken, Bram Platel:
Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation. ISBI 2016: 1414-1417 - [c83]Koen Vijverberg, Mohsen Ghafoorian, Inge W. M. van Uden, Frank-Erik de Leeuw, Bram Platel, Tom Heskes:
A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation. Computer-Aided Diagnosis 2016: 97851C - [c82]Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, Tom Heskes:
Computing Lower and Upper Bounds on the Probability of Causal Statements. Probabilistic Graphical Models 2016: 487-498 - [c81]Ruifei Cui, Perry Groot, Tom Heskes:
Copula PC Algorithm for Causal Discovery from Mixed Data. ECML/PKDD (2) 2016: 377-392 - [i17]Harm Berntsen, Wouter Kuijper, Tom Heskes:
The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection. CoRR abs/1604.04428 (2016) - [i16]Ridho Rahmadi, Perry Groot, Marieke M. H. C. van Rijn, Jan A. J. G. van den Brand, Marianne Heins, Hans Knoop, Tom Heskes:
Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling. CoRR abs/1605.06838 (2016) - [i15]Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Inge van Uden, Clara I. Sánchez, Geert Litjens, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel:
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities. CoRR abs/1610.04834 (2016) - [i14]Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Mayra Bergkamp, Joost Wissink, Jiri Obels, Karlijn Keizer, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel:
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin. CoRR abs/1610.07442 (2016) - 2015
- [j48]Christiaan A. de Leeuw, Joris M. Mooij, Tom Heskes, Danielle Posthuma:
MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLoS Comput. Biol. 11(4) (2015) - [j47]Max Hinne, Ronald J. Janssen, Tom Heskes, Marcel A. J. van Gerven:
Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates. PLoS Comput. Biol. 11(11) (2015) - [j46]Adriana Birlutiu, Florence d'Alché-Buc, Tom Heskes:
A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions. IEEE ACM Trans. Comput. Biol. Bioinform. 12(3): 538-550 (2015) - [c80]Elena Sokolova, Perry Groot, Tom Claassen, Daniel von Rhein, Jan K. Buitelaar, Tom Heskes:
Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data. AIME 2015: 177-181 - [c79]Laurens Wiel, Tom Heskes, Evgeni Levin:
KeCo: Kernel-Based Online Co-agreement Algorithm. Discovery Science 2015: 308-315 - [c78]Fabian Gieseke, Tapio Pahikkala, Tom Heskes:
Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. IDA 2015: 95-107 - [c77]Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, Tom Heskes:
Domain Generalization Based on Transfer Component Analysis. IWANN (1) 2015: 325-334 - [c76]Mohsen Ghafoorian, Nico Karssemeijer, Inge van Uden, Frank-Erik de Leeuw, Tom Heskes, Elena Marchiori, Bram Platel:
Small white matter lesion detection in cerebral small vessel disease. Computer-Aided Diagnosis 2015: 941411 - [c75]Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes:
Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling. AALTD@PKDD/ECML 2015 - [c74]Sanne Schoenmakers, Tom Heskes, Marcel van Gerven:
Hidden Markov Models for Reading Words from the Human Brain. PRNI 2015: 89-92 - [e2]Marina Meila, Tom Heskes:
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, July 12-16, 2015, Amsterdam, The Netherlands. AUAI Press 2015, ISBN 978-0-9966431-0-8 [contents] - [i13]Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes:
Causality on Cross-Sectional Data: Stable Specification Search in Constrained Structural Equation Modeling. CoRR abs/1506.05600 (2015) - [i12]Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes:
Bigger Buffer k-d Trees on Multi-Many-Core Systems. CoRR abs/1512.02831 (2015) - 2014
- [j45]Syed Abbas, Tjeerd Dijkstra, Tom Heskes:
A comparative study of cell classifiers for image-based high-throughput screening. BMC Bioinform. 15: 342 (2014) - [j44]Tom Heskes, Rob Eisinga, Rainer Breitling:
A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments. BMC Bioinform. 15: 367 (2014) - [j43]Ronald J. Janssen, Max Hinne, Tom Heskes, Marcel A. J. van Gerven:
Quantifying uncertainty in brain network measures using Bayesian connectomics. Frontiers Comput. Neurosci. 8: 126 (2014) - [j42]Sanne Schoenmakers, Umut Güçlü, Marcel van Gerven, Tom Heskes:
Gaussian mixture models and semantic gating improve reconstructions from human brain activity. Frontiers Comput. Neurosci. 8: 173 (2014) - [j41]Jesse Alama, Tom Heskes, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban:
Premise Selection for Mathematics by Corpus Analysis and Kernel Methods. J. Autom. Reason. 52(2): 191-213 (2014) - [j40]Max Hinne, Luca Ambrogioni, Ronald J. Janssen, Tom Heskes, Marcel A. J. van Gerven:
Structurally-informed Bayesian functional connectivity analysis. NeuroImage 86: 294-305 (2014) - [c73]Binyam Gebrekidan Gebre, Onno Crasborn, Peter Wittenburg, Sebastian Drude, Tom Heskes:
Unsupervised Feature Learning for Visual Sign Language Identification. ACL (2) 2014: 370-376 - [c72]Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes, Sebastian Drude:
Motion history images for online speaker/signer diarization. ICASSP 2014: 1537-1541 - [c71]Mike Koeman, Tom Heskes:
Mutual Information Estimation with Random Forests. ICONIP (2) 2014: 524-531 - [c70]Binyam Gebrekidan Gebre, Peter Wittenburg, Sebastian Drude, Marijn Huijbregts, Tom Heskes:
Speaker diarization using gesture and speech. INTERSPEECH 2014: 582-586 - [c69]Elena Sokolova, Perry Groot, Tom Claassen, Tom Heskes:
Causal Discovery from Databases with Discrete and Continuous Variables. Probabilistic Graphical Models 2014: 442-457 - [c68]Adriana Birlutiu, Tom Heskes:
Using Topology Information for Protein-Protein Interaction Prediction. PRIB 2014: 10-22 - [c67]Sanne Schoenmakers, Marcel van Gerven, Tom Heskes:
Gaussian mixture models improve fMRI-based image reconstruction. PRNI 2014: 1-4 - [i11]Botond Cseke, Tom Heskes:
Properties of Bethe Free Energies and Message Passing in Gaussian Models. CoRR abs/1401.3877 (2014) - 2013
- [j39]Adriana Birlutiu, Perry Groot, Tom Heskes:
Efficiently learning the preferences of people. Mach. Learn. 90(1): 1-28 (2013) - [j38]Diego Vidaurre, Marcel A. J. van Gerven, Concha Bielza, Pedro Larrañaga, Tom Heskes:
Bayesian Sparse Partial Least Squares. Neural Comput. 25(12): 3318-3339 (2013) - [j37]Max Hinne, Tom Heskes, Christian F. Beckmann, Marcel A. J. van Gerven:
Bayesian inference of structural brain networks. NeuroImage 66: 543-552 (2013) - [j36]Sanne Schoenmakers, Markus Barth, Tom Heskes, Marcel van Gerven:
Linear reconstruction of perceived images from human brain activity. NeuroImage 83: 951-961 (2013) - [c66]Binyam Gebrekidan Gebre, Marcos Zampieri, Peter Wittenburg, Tom Heskes:
Improving Native Language Identification with TF-IDF Weighting. BEA@NAACL-HLT 2013: 216-223 - [c65]Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes:
Automatic Signer Diarization - The Mover Is the Signer Approach. CVPR Workshops 2013: 283-287 - [c64]Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes:
The gesturer is the speaker. ICASSP 2013: 3751-3755 - [c63]Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes:
Automatic sign language identification. ICIP 2013: 2626-2630 - [c62]Tom Claassen, Tom Heskes:
Bayesian Probabilities for Constraint-Based Causal Discovery. IJCAI 2013: 2992-2996 - [c61]Evgeni Tsivtsivadze, Tom Heskes, Armand Paauw:
Multi-view Multi-class Classification for Identification of Pathogenic Bacterial Strains. MCS 2013: 61-72 - [c60]Evgeni Tsivtsivadze, Hanneke Borgdorff, Janneke van de Wijgert, Frank H. J. Schuren, Rita Verhelst, Tom Heskes:
Neighborhood Co-regularized Multi-view Spectral Clustering of Microbiome Data. PSL 2013: 80-90 - [c59]Tom Claassen, Joris M. Mooij, Tom Heskes:
Learning Sparse Causal Models is not NP-hard. UAI 2013 - [c58]Joris M. Mooij, Tom Heskes:
Cyclic Causal Discovery from Continuous Equilibrium Data. UAI 2013 - [i10]Tom Heskes, Onno Zoeter:
Expectation Propogation for approximate inference in dynamic Bayesian networks. CoRR abs/1301.0572 (2013) - [i9]Wim Wiegerinck, Tom Heskes:
IPF for Discrete Chain Factor Graphs. CoRR abs/1301.0613 (2013) - [i8]Evgeni Tsivtsivadze, Tom Heskes:
Semi-supervised Ranking Pursuit. CoRR abs/1307.0846 (2013) - [i7]Tom Claassen, Joris M. Mooij, Tom Heskes:
Learning Sparse Causal Models is not NP-hard. CoRR abs/1309.6824 (2013) - [i6]Joris M. Mooij, Tom Heskes:
Cyclic Causal Discovery from Continuous Equilibrium Data. CoRR abs/1309.6849 (2013) - 2012
- [j35]L. Niels Cornelisse, Evgeni Tsivtsivadze, Marieke Meijer, Tjeerd Dijkstra, Tom Heskes, Matthijs Verhage:
Molecular Machines in the Synapse: Overlapping Protein Sets Control Distinct Steps in Neurosecretion. PLoS Comput. Biol. 8(4) (2012) - [c57]Daniel Küh