Publications

Stefan Lüdtke, Christian Bartelt, und Heiner Stuckenschmidt. Outlying aspect mining via sum-product networks. Advances in Knowledge Discovery and Data Mining, 27--38, Springer Nature Switzerland, Cham, 2023. [PUMA: Outlying Xack aspect networks sum-product]

Chengjin Xu, Fenglong Su, und Jens Lehmann. Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs. 2022. [PUMA: Alignment Entity Graph Graphs Knowledge Networks Neural Temporal Time-aware Yaff] URL

Victor Jüttner, Martin Grimmer, und Erik Buchmann. ChatIDS: Explainable Cybersecurity Using Generative AI. 2023. [PUMA: ChatGPT Detection Intrusion Networks Yaff]

Martin Bogdan. Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse. XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja), 201--207, Servizo de Publicacións da UDC, September 2022. [PUMA: Learning Xack algorithms learning networks neural neurological plausible spiking]

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 remote sensing vision] URL

Marc Hellmuth, David Schaller, und Peter F. Stadler. Clustering systems of phylogenetic networks. Theory in Biosciences, (142)4:301–358, Springer Science and Business Media LLC, August 2023. [PUMA: Clustering Zno networks phylogenetic systems] URL

Christoph Flamm, Stefan Müller, und Peter F. Stadler. Every atom-atom map can be explained by electron pushing diagrams. arXiv, 2023. [PUMA: (math.CO), (q-bio.MN), 05C92, 92E10 Biological Combinatorics FOS: Mathematics, Molecular Networks sciences, yaff] URL

Johannes R. Schmidt, Janine Haupt, Sina Riemschneider, Christoph Kämpf, Dennis Löffler, Conny Blumert, Kristin Reiche, Ulrike Koehl, Stefan Kalkhof, und Jörg Lehmann. Transcriptomic signatures reveal a shift towards an anti-inflammatory gene expression profile but also the induction of type I and type II interferon signaling networks through aryl hydrocarbon receptor activation in murine macrophages. Frontiers in Immunology, (14)Frontiers Media SA, Mai 2023. [PUMA: Transcriptomic Zno activation anti-inflammatory aryl expression gene hydrocarbon induction interferon macrophages murine networks profile receptor signaling signatures through] URL

Aris Marcolongo, Mykhailo Vladymyrov, Sebastian Lienert, Nadav Peleg, Sigve Haug, und Jakob Zscheischler. Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks. Environmental Data Science, (1):e2, 2022. [PUMA: Convolutional Networks Neural Predicting data gross low primary production weather]

Adrian Lindenmeyer, Malte Blattmann, Stefan Franke, Thomas Neumuth, und Daniel Schneider. Inadequacy of common stochastic neural networks for reliable clinical decision support. 2024. [PUMA: Inadequacy clinical decision networks neural reliable stochastic support] URL

Peter Winkler, Norman Koch, Andreas Hornig, und Johannes Gerritzen. OmniOpt – A tool for hyperparameter optimization on HPC. In Heike Jagode, Hartwig Anzt, Hatem Ltaief, und Piotr Luszczek (Hrsg.), High Performance Computing - ISC High Performance Digital 2021 International Workshops, 2021, Revised Selected Papers, 285--296, Springer, Berlin u. a., Germany, 13.11.2021. [PUMA: FIS_scads High Hyperparameter Neural computing, networks optimization, performance]

Nishant Kumar, Lukas Krause, Thomas Wondrak, Sven Eckert, Kerstin Eckert, und Stefan Gumhold. Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks. Sensors, (24)42024. [PUMA: topic_visualcomputing Density Flux Fraction Invertible Magnetic Networks Neural Noisy Reconstruction Void] URL

Timo P. Gros, David Groß, Stefan Gumhold, Jörg Hoffmann, Michaela Klauck, und Marcel Steinmetz. TraceVis: Towards Visualization for Deep Statistical Model Checking. Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20–30, 2020, Proceedings, Part IV, 27–46, Springer-Verlag, Berlin, Heidelberg, 2020. [PUMA: Checking Model Networks Neural Statistical Visualization] URL

Julia Scheel, Matti Hoch, Markus Wolfien, und Shailendra Gupta. NaviCenta - The disease map for placental research. Placenta, (143):12--15, Elsevier BV, November 2023. [PUMA: topic_lifescience Molecular Networks Omics Systems analysis; biology integration; interaction map;]