Publications

Lester Kalms, Matthias Nickel, and Diana Göhringer. ArcvaVX: OpenVX Framework for Adaptive Reconfigurable Computer Vision Architectures. In Francesca Palumbo, Georgios Keramidas, Nikolaos Voros, and Pedro C. Diniz (Eds.), Applied Reconfigurable Computing. Architectures, Tools, and Applications - 19th International Symposium, ARC 2023, Proceedings, 97--112, Springer Science and Business Media B.V., Germany, 2023. [PUMA: FIS_scads topic_federatedlearn OpenVX FPGA, Computer Vision, HLS, Framework,] URL

Lucas Lange, Maurice-Maximilian Heykeroth, and Erhard Rahm. Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning. arXiv preprint arXiv:2409.01329, arXiv, September 2024. [PUMA: ep FOS: Machine sciences (cs.LG), Security Learning (cs.CV), and Vision Computer (cs.CR), Cryptography information Pattern area_bigdata area_responsibleai Recognition]

Markus Bauer, and Christoph Augenstein. Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.. Proceedings of the 18th Conference on Computer Science and Intelligence Systems, (35):93–101, IEEE, September 2023. [PUMA: Study Learning Computer Data Self-Supervised Unlabelled Vision. Comparative] URL

Marianne Maktabi, Hannes Köhler, Magarita Ivanova, Thomas Neumuth, Nada Rayes, Lena Seidemann, Robert Sucher, Boris Jansen-Winkeln, Ines Gockel, Manuel Barberio, and Claire Chalopin. Classification of hyperspectral endocrine tissue images using support vector machines. Int. J. Med. Robot., (16)5:1--10, Wiley, October 2020. [PUMA: intraoperative thyroidectomy imaged guided computer neck; surgery; and imaging; assisted head]

Matti Wiegmann, Jennifer Rakete, Magdalena Wolska, Benno Stein, and Martin Potthast. If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level. arXiv, 2024. [PUMA: sciences, FOS: Computers sciences (cs.CY), Computation topic_language Society and Computer Language (cs.CL), information] URL

Katja Hoffmann, Katja Cazemier, Christoph Baldow, Silvio Schuster, Yuri Kheifetz, Sibylle Schirm, Matthias Horn, Thomas Ernst, Constanze Volgmann, Christian Thiede, Andreas Hochhaus, Martin Bornhäuser, Meinolf Suttorp, Markus Scholz, Ingmar Glauche, Markus Loeffler, and Ingo Roeder. Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology. BMC Med. Inform. Decis. Mak., (20)1:28, February 2020. [PUMA: decision-making; workflow; Haematology; Support Mathematical therapy Clinical Data Routine treatment management; simulation; modelling; system optimization; Individual Computer planning; Model-based]

Kim Breitwieser, Allison Lahnala, Charles Welch, Lucie Flek, and Martin Potthast. Modeling Proficiency with Implicit User Representations. arXiv, 2021. [PUMA: sciences Computation and Computer sciences, Language (cs.CL), information FOS:] URL

Najdet Charaf, Julian Haase, Adrian Kulisch, Christian Von Elm, and Diana Göhringer. RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures. 2023 26th Euromicro Conference on Digital System Design (DSD), 585--592, IEEE, Sep 8, 2023. [PUMA: FIS_scads Shape learning Machine computing, topic_federatedlearn Runtime, vision, algorithms, architecture, Embedded Computer Scalability,] URL

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, and Christian L. Müller. Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (physics.data-an), Analysis, Probability Data sciences, Numerical Statistics FOS: Machine Physical sciences Analysis (cs.LG), Learning (math.NA), Mathematics, and Computer information] URL