Publications

Jürgen Jost, und Guillermo Restrepo. Self-reinforcing mechanisms driving the evolution of the chemical space. Perspect. Sci., 1--39, MIT Press, Juli 2023. [PUMA: Self-reinforcing Yaff chemical driving space]

Bärbel Hanle, Frank Loebe, Patryk Burek, und Heinrich Herre. Balls and Universal Space in GFO.. JOWO, 2023. [PUMA: GFO Top-level Xack metric of ontology space universal]

Michael Gleicher, Maria Riveiro, Tatiana von Landesberger, Oliver Deussen, Remco Chang, Christina Gillman, und Theresa-Marie Rhyne. A problem space for designing visualizations. IEEE Comput. Graph. Appl., (43)4:111--120, Juli 2023. [PUMA: Zno designing problem space visualizations]

Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, und Olaf Wolkenhauer. ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. Pattern Recognition, (147):110138, 2024. [PUMA: Convex GAN Imbalanced LoRAS Tabular data learning space] URL

Raimund Dachselt, Sarah Alice Gaggl, Markus Krötzsch, Julián Méndez, Dominik Rusovac, und Mei Yang. NEXAS: A Visual Tool for Navigating and Exploring Argumentation Solution Spaces. In Francesca Toni, Sylwia Polberg, Richard Booth, Martin Caminada, und Hiroyuki Kido (Hrsg.), Proceedings of the 9th International Conference on Computational Models of Argument (COMMA 2022), (220146):116–127, IOS Press, Amsterdam u. a., Netherlands, 01.09.2022. [PUMA: topic_knowledge FIS_scads abstract argumentation, exploration, solution space visualization]

Veronia Iskandar, Mohamed A. Abd El Ghany, und Diana Goehringer. NDP-RANK: Prediction and ranking of NDP systems performance using machine learning. Microprocessors and Microsystems, (96):104707, 2023. [PUMA: topic_federatedlearn Design Machine Modeling, Near-data Prediction, exploration learning, processing, space] URL