Deep Representation and Metric Learning
Developing common approaches for designing and learning representations and metrics for a specific domain or downstream task.
Publications
2024[ to top ]
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Global Vegetation Modeling with Pre-Trained Weather Transformers. . 2024.
2023[ to top ]
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Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks. . In LWDA’23: Lernen, Wissen, Daten, Analysen. October 09--11, 2023, Marburg, Germany, M. Leyer (ed.). 2023.
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Optimizing Medical Service Request Processes through Language Modeling and Semantic Search. . In 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), of ICMHI 2023, pp. 136–141. Association for Computing Machinery, Kyoto, Japan, 2023.
2019[ to top ]
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On the Right Track! Analysing and Predicting Navigation Success in Wikipedia. . In Proceedings of the 30th ACM Conference on Hypertext and Social Media, of HT ’19, pp. 143–152. ACM, Hof, Germany, 2019.
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Classification of text-types in german novels. . In Digital Humanities 2019: Conference Abstracts. 2019.
2016[ to top ]
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Extracting Semantics from Unconstrained Navigation on Wikipedia. . In KI, 30(2), pp. 163–168. 2016.
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Straight Talk! Automatic Recognition of Direct Speech in Nineteenth-Century French Novels. . In DH, pp. 346–353. 2016.