1 code implementation • LREC 2022 • Florian Borchert, Christina Lohr, Luise Modersohn, Jonas Witt, Thomas Langer, Markus Follmann, Matthias Gietzelt, Bert Arnrich, Udo Hahn, Matthieu-P. Schapranow
Despite remarkable advances in the development of language resources over the recent years, there is still a shortage of annotated, publicly available corpora covering (German) medical language.
1 code implementation • Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis 2024 • Silvan Wehrli, Chisom Ezekannagha, Georges Hattab, Tamara Boender, Bert Arnrich, Christopher Irrgang
Social media are a critical component of the information ecosystem during public health crises.
1 code implementation • 5 Jan 2024 • Silvan Wehrli, Bert Arnrich, Christopher Irrgang
This work introduces a benchmark assessing the performance of clustering German text embeddings in different domains.
1 code implementation • 17 Oct 2023 • Florian Borchert, Ignacio Llorca, Roland Roller, Bert Arnrich, Matthieu-P. Schapranow
Weakly supervised cross-encoders are effective when no training data is available for the target task.
no code implementations • 6 Jul 2023 • Orhan Konak, Alexander Wischmann, Robin van de Water, Bert Arnrich
This research significantly advances the field of Human Activity Recognition by providing a lightweight, on-device solution for determining the optimal sensor placement, thereby enhancing data anonymization and supporting a multimodal classification approach.
4 code implementations • 8 Jun 2023 • Robin van de Water, Hendrik Schmidt, Paul Elbers, Patrick Thoral, Bert Arnrich, Patrick Rockenschaub
Datasets and code are not always published, and cohort definitions, preprocessing pipelines, and training setups are difficult to reproduce.
1 code implementation • 21 Nov 2022 • Bjarne Pfitzner, Bert Arnrich
Federated learning (FL) is getting increased attention for processing sensitive, distributed datasets common to domains such as healthcare.
2 code implementations • 6 May 2022 • Joceline Ziegler, Bjarne Pfitzner, Heinrich Schulz, Axel Saalbach, Bert Arnrich
We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients.
1 code implementation • 1 Nov 2021 • Jossekin Beilharz, Bjarne Pfitzner, Robert Schmid, Paul Geppert, Bert Arnrich, Andreas Polze
Federated learning allows a group of distributed clients to train a common machine learning model on private data.