Publications

Maksim Kukushkin, Martin Bogdan, und Thomas Schmid. On optimizing morphological neural networks for hyperspectral image classification. In Wolfgang Osten (Hrsg.), 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, und 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

Siavash Ghiasvand, und Florina M. Ciorba. Anomaly Detection in High Performance Computers: A Vicinity Perspective. 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC), 112--120, Amsterdam, Netherlands, Juni 2019. [PUMA: Anomaly Bridges, Computer Correlation, Graphics HPC Hardware, Resource analysis, anomaly anonymized approach approach, architecture, components, computerised computers, computing computing, detection detection, exascale failure hardware, high hpc, instrumentation, management, mechanism, myOwn node performance prediction, privacy, processing sensors, statistical system system, units, vicinity, vicinity-based,]

Tom Richard Vargis, und Siavash Ghiasvand. A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement. The International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, Texas, USA, Januar 2022. [PUMA: Cluster Computer Computing, Distributed, Learning Machine Parallel, Science and myOwn] URL

Tom Richard Vargis, und Siavash Ghiasvand. Content-Aware Depth-Adaptive Image Restoration. Proceedings of the 29th International Conference on Automation and Computing, Sunderland, UK, Januar 2024. [PUMA: Computer Learning, Machine Pattern Recognition, Science Vision and myOwn] URL

Markus Bauer, und 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

Oscar J. Pellicer-Valero, Miguel-Ángel Fernández-Torres, Chaonan Ji, Miguel D. Mahecha, und Gustau Camps-Valls. Explainable Earth Surface Forecasting under Extreme Events. arXiv, 2024. [PUMA: (cs.LG), Computer FOS: Learning Machine and information sciences sciences, topic_earthenvironment] URL

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

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, und 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, und 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, und 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, und Claire Chalopin. Classification of hyperspectral endocrine tissue images using support vector machines. Int. J. Med. Robot., (16)5:1--10, Wiley, Oktober 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, und 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, Februar 2020. [PUMA: Clinical Computer Data Haematology; Individual Mathematical Model-based Routine Support decision-making; management; modelling; optimization; planning; simulation; system therapy treatment workflow;]