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Engineering & Materials Science
# Big Data
# Data Integration
# Machine Learning
# Metadata
# Ontology
# Radiology
# Semantics
# Uncertainty
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Publicaties
Recent
Maas, J., Römer, J. G. W. T.
, Baysal Erez, I.
, & van Keulen, M. (2023).
Investigating Imputation Methods for Handling Missing Data. Poster session presented at Joint International Scientific Conferences on AI and Machine Learning, BNAIC/BeNeLearn 2023, Delft, Netherlands.
Maas, J., Römer, J. G. W. T.
, Baysal Erez, I.
, & van Keulen, M. (2023).
Investigating Imputation Methods for Handling Missing Data. Paper presented at Joint International Scientific Conferences on AI and Machine Learning, BNAIC/BeNeLearn 2023, Delft, Netherlands.
Xiao, Q.
, Wu, B., Yin, L.
, van Keulen, M., & Pechenizkiy, M. (2023).
Can Less Yield More? Insights into Truly Sparse Training. Poster session presented at ICLR 2023 Workshop on Sparsity in Neural Networks, Kigali, Rwanda.
https://drive.google.com/file/d/1kbWZ9ejU9XvtOMRtAcVYmcoRCDIWj3zy/view
Nauta, M., Schlötterer, J.
, van Keulen, M.
, & Seifert, C. (2023).
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. Abstract from 2nd Explainable AI for Computer Vision Workshop, XAI4CV 2023, Vancouver, British Columbia, Canada.
Nauta, M., Schlötterer, J.
, van Keulen, M.
, & Seifert, C. (2023).
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. In
CVPR 2023 (pp. 2744-2753)
Nauta, M. (2023).
Explainable AI and Interpretable Computer Vision: From Oversight to Insight. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente.
https://doi.org/10.3990/1.9789036555753
Tran, T. H. A., Wiesner, M. L.
, & van Keulen, M. (2022).
Influence of discretization granularity on learning classification models. Paper presented at BNAIC/BeNeLearn 2022 Joint International Scientific Conferences on AI and Machine Learning, Mechelen, Belgium.
https://bnaic2022.uantwerpen.be/BNAICBeNeLearn_2022_submission_8652
Nauta, M., Trienes, J.
, Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J.
, van Keulen, M.
, & Seifert, C. (2022).
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ArXiv.org.
https://doi.org/10.48550/arXiv.2201.08164
Yenidogan, B.
, Pathak, S., Geerdink, J., Hegeman, J. H.
, & Van Keulen, M. (2022).
Multimodal Machine Learning for 30-Days Post-Operative Mortality Prediction of Elderly Hip Fracture Patients. In B. Xue, M. Pechenizkiy, & Y. S. Koh (Eds.),
Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 (pp. 508-516). (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2021-December). IEEE.
https://doi.org/10.1109/ICDMW53433.2021.00068
Baysal Erez, I.
, & van Keulen, M. (2022).
Understanding dynamic sparse training capabilities in accommodating sparse data. 1-6. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2022, Grenoble, France.
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Vakken Collegejaar 2023/2024
Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.
Vakken Collegejaar 2022/2023
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Contactgegevens
+31534893453
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Bezoekadres
Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
(gebouwnr. 11), kamer 4061
Hallenweg 19
7522NH Enschede
Postadres
Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
4061
Postbus 217
7500 AE Enschede