Publications

Maksim Kukushkin, Martin Bogdan, and Thomas Schmid. On optimizing morphological neural networks for hyperspectral image classification. In Wolfgang Osten (Eds.), Sixteenth International Conference on Machine Vision (ICMV 2023), (13072):1307202, SPIE, 2024. [PUMA: classification computer deep hyperspectral image learning mathematical morphological morphology networks neuronal nopdf remote sensing vision] URL

Andre de Carvalho, Robson Bonidia, Jude Dzevela Kong, Mariana Dauhajre, Claudio Struchiner, Guilherme Goedert, Peter F. Stadler, Maria Emilia Walter, Danilo Sanches, Troy Day, Marcia Castro, John Edmunds, Manuel Colome-Hidalgo, Demian Arturo Herrera Morban, Edian F. Franco, Cesar Ugarte-Gil, Patricia Espinoza-Lopez, Gabriel Carrasco-Escobar, and Ulisses Rocha. Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries. arXiv, 2024. [PUMA: Artificial Computer FOS Intelligence Zno and information sciences] URL

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: Comparative Computer Data Learning Self-Supervised Study Unlabelled Vision yaff] 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: (cs.CR), (cs.CV), (cs.LG), Computer Cryptography FOS: Learning Machine Pattern Recognition Security Vision and area_bigdata area_responsibleai ep information sciences xack yaff]

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: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences,] URL

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: topic_language (cs.CL), (cs.CY), Computation Computer Computers FOS: Language Society and information sciences sciences,] URL

Kim Breitwieser, Allison Lahnala, Charles Welch, Lucie Flek, and Martin Potthast. Modeling Proficiency with Implicit User Representations. arXiv, 2021. [PUMA: (cs.CL), Computation Computer FOS: Language and information sciences sciences,] 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: and assisted computer guided head imaged imaging; intraoperative neck; surgery; thyroidectomy]

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: Clinical Computer Data Haematology; Individual Mathematical Model-based Routine Support decision-making; management; modelling; optimization; planning; simulation; system therapy treatment workflow;]