Eris van Twist, Floor W. Hiemstra, Arnout B.G. Cramer, Sascha C.A.T. Verbruggen, David M.J. Tax, Koen Joosten, Maartje Louter, Dirk C.G. Straver, Matthijs de Hoog, More Authors (2024), An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children, In Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Volume 20 p.389-397.
Kim van den Houten, David M.J. Tax, Esteban Freydell, Mathijs de Weerdt (2024), Learning from Scenarios for Repairable Stochastic Scheduling, Bistra Dilkina (Eds.), In Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 21st International Conference, CPAIOR 2024, Proceedings p.234-242, Springer.
Aleksandr Dekhovich, David M.J. Tax, Marcel H.F. Sluiter, Miguel A. Bessa (2024), Neural network relief: a pruning algorithm based on neural activity, In Machine Learning Volume 113 p.2597-2618.
Ramin Ghorbani, Marcel J.T. Reinders, David M.J. Tax (2024), PATE: Proximity-Aware Time Series Anomaly Evaluation, In KDD '24 p.872-883, Association for Computing Machinery (ACM).
Ramin Ghorbani, Marcel J.T. Reinders, David M.J. Tax (2024), Personalized anomaly detection in PPG data using representation learning and biometric identification, In Biomedical Signal Processing and Control Volume 94.
Aleksandr Dekhovich, Marcel H.F. Sluiter, David M.J. Tax, Miguel A. Bessa (2024), iPINNs: incremental learning for Physics-informed neural networks, In Engineering with Computers.
Yuko Kato, David M.J. Tax, Marco Loog (2023), A View on Model Misspecification in Uncertainty Quantification, Toon Calders, Bart Goethals, Celine Vens, Jefrey Lijffijt (Eds.), In Artificial Intelligence and Machine Learning - 34th Joint Benelux Conference, BNAIC/Benelearn 2022, Revised Selected Papers p.65-77, Springer.
Michiel Bongaerts, Purva Kulkarni, Alan Zammit, Ramon Bonte, Leo A. J. Kluijtmans, Henk J. Blom, Udo F. H. Engelke, D.M.J. Tax, George J.G. Ruijter, M.J.T. Reinders (2023), Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data, In Metabolites Volume 13.
Aleksandr Dekhovich, David M.J. Tax, Marel H.F. Sluiter, Miguel A. Bessa (2023), Continual prune-and-select: class-incremental learning with specialized subnetworks, In Applied Intelligence Volume 53 p.17849-17864.
Arman Naseri, David Tax, Pim van der Harst, Marcel Reinders, Ivo van der Bilt (2023), Data-efficient machine learning methods in the ME-TIME study: Rationale and design of a longitudinal study to detect atrial fibrillation and heart failure from wearables, In Cardiovascular Digital Health Journal Volume 4 p.165-172.