Deutsch Intern
    Data Science Chair

    Deep Representation and Metric Learning

    Developing common approaches for designing and learning representations and metrics for a specific domain or downstream task.


    2024[ to top ]
    • Global Vegetation Modeling with Pre-Trained Weather Transformers. Janetzky, Pascal; Gallusser, Florian; Hentschel, Simon; Hotho, Andreas; Krause, Anna. 2024.
    2023[ to top ]
    • Liquor-HGNN: A heterogene...
      Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks. Schaller, Melanie; Steininger, Michael; Dulny, Andrzej; Schlör, Daniel; Hotho, Andreas. In LWDA’23: Lernen, Wissen, Daten, Analysen. October 09--11, 2023, Marburg, Germany, M. Leyer (ed.). 2023.
    • Optimizing Medical Servic...
      Optimizing Medical Service Request Processes through Language Modeling and Semantic Search. Schlör, Daniel; Pfister, Jan; Hotho, Andreas. 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.
    2021[ to top ]
    • Do Different Deep Metric ...
      Do Different Deep Metric Learning Losses Lead to Similar Learned Features?. Kobs, Konstantin; Steininger, Michael; Dulny, Andrzej; Hotho, Andreas. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10644–10654. 2021.
    2020[ to top ]
    • SimLoss: Class Similariti...
      SimLoss: Class Similarities in Cross Entropy. Kobs, Konstantin; Steininger, Michael; Zehe, Albin; Lautenschlager, Florian; Hotho, Andreas. 2020.
    2019[ to top ]
    • On the Right Track! Analy...
      On the Right Track! Analysing and Predicting Navigation Success in Wikipedia. Koopmann, Tobias; Dallmann, Alexander; Hettinger, Lena; Niebler, Thomas; Hotho, Andreas. In Proceedings of the 30th ACM Conference on Hypertext and Social Media, of HT ’19, pp. 143–152. ACM, Hof, Germany, 2019.
    • Classification of text-ty...
      Classification of text-types in german novels. Schlör, D; Schöch, C; Hotho, A. In Digital Humanities 2019: Conference Abstracts. 2019.
    2017[ to top ]
    • Learning Semantic Related...
      Learning Semantic Relatedness From Human Feedback Using Metric Learning. Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas. 2017.
    • Learning Word Embeddings ...
      Learning Word Embeddings from Tagging Data: A methodological comparison. Niebler, Thomas; Hahn, Luzian; Hotho, Andreas. In Proceedings of the LWDA. 2017.
    2016[ to top ]
    • Extracting Semantics from...
      Extracting Semantics from Unconstrained Navigation on Wikipedia. Niebler, Thomas; Schlör, Daniel; Becker, Martin; Hotho, Andreas. In KI, 30(2), pp. 163–168. 2016.
    • Straight Talk! Automatic ...
      Straight Talk! Automatic Recognition of Direct Speech in Nineteenth-Century French Novels. Schöch, Christof; Schlör, Daniel; Popp, Stefanie; Brunner, Annelen; Henny, Ulrike; Tello, Jos’e} Calvo. In DH, pp. 346–353. 2016.
    2015[ to top ]
    • ConDist: A Context-Driven Categorical Distance Measure. Ring, Markus; Otto, Florian; Becker, Martin; Niebler, Thomas; Landes, Dieter; Hotho, Andreas. ECMLPKDD2015 (ed.). 2015.
    2013[ to top ]
    • Computing semantic relate...
      Computing semantic relatedness from human navigational paths on Wikipedia. Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas. In Proceedings of the 22nd international conference on World Wide Web companion, of WWW ’13 Companion, ACM (ed.), pp. 171–172. International World Wide Web Conferences Steering Committee, Rio de Janeiro, Brazil, 2013.
    • Computing Semantic Relate...
      Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia. Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas. In International Journal on Semantic Web and Information Systems (IJSWIS), 9(4), pp. 41–70. IGI Global, 2013.