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Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents.

O. Moulin, V. François-Lavet, P. Elbers, and M. Hoogendoorn. WI/IAT, page 166-173. IEEE, (2022)

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Martin G W Zeppenfeld

Sverrir G W Schopka

Winfried Elbers

Stefan Elbers

Bruno Elbers

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Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents.O. Moulin, V. François-Lavet, P. Elbers, and M. Hoogendoorn. WI/IAT, page 166-173. IEEE, (2022)Guideline-informed reinforcement learning for mechanical ventilation in critical care.F. den Hengst, M. Otten, P. Elbers, F. van Harmelen, V. François-Lavet, and M. Hoogendoorn. Artif. Intell. Medicine, (January 2024)Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems.A. Curth, P. Thoral, W. van den Wildenberg, P. Bijlstra, D. de Bruin, P. Elbers, and M. Fornasa. PKDD/ECML Workshops (1), volume 1167 of Communications in Computer and Information Science, page 605-621. Springer, (2019)Prediction of Acute Kidney Injury in the Intensive Care Unit: Preliminary Findings in a European Open Access Database.M. Fujarski, C. Porschen, L. Plagwitz, A. Brenner, N. Ghoreishi, P. Thoral, H. de Grooth, P. Elbers, R. Weiss, M. Meersch and 3 other author(s). MIE, volume 294 of Studies in Health Technology and Informatics, page 139-140. IOS Press, (2022)Improving Generalization in Reinforcement Learning Through Forked Agents.O. Moulin, V. François-Lavet, P. Elbers, and M. Hoogendoorn. IEA/AIE (2), volume 13926 of Lecture Notes in Computer Science, page 249-260. Springer, (2023)Prognostic models of in-hospital mortality of intensive care patients using neural representation of unstructured text: A systematic review and critical appraisal.I. Vagliano, N. Dormosh, M. Rios, T. Luik, T. Buonocore, P. Elbers, D. Dongelmans, M. Schut, and A. Abu-Hanna. J. Biomed. Informatics, (October 2023)Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.J. Verhaeghe, T. Corte, C. Sauer, T. Hendriks, O. Thijssens, F. Ongenae, P. Elbers, J. Waele, and S. Hoecke. Int. J. Medical Informatics, (July 2023)Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification.J. Jordon, D. Jarrett, E. Saveliev, J. Yoon, P. Elbers, P. Thoral, A. Ercole, C. Zhang, D. Belgrave, and M. van der Schaar. NeurIPS (Competition and Demos), volume 133 of Proceedings of Machine Learning Research, page 206-215. PMLR, (2020)Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression.Z. Qian, W. Zame, L. Fleuren, P. Elbers, and M. van der Schaar. NeurIPS, page 11364-11383. (2021)Predicting readmission or death after discharge from the ICU: External validation and retraining of a machine learning model.A. Hond, I. Kant, M. Fornasa, G. Cinà, P. Elbers, P. Thoral, M. Arbous, and E. Steyerberg. AMIA, AMIA, (2022)

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Disambiguation of "Elbers, Paul W. G."

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Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents.

Please choose a person to relate this publication to

Martin G W Zeppenfeld

Sverrir G W Schopka

Winfried Elbers

Stefan Elbers

Bruno Elbers

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Elbers, Paul W. G."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents.

Please choose a person to relate this publication to

Martin G W Zeppenfeld

Sverrir G W Schopka

Winfried Elbers

Stefan Elbers

Bruno Elbers

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
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Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents.