Publications

Chengjin Xu, Fenglong Su, and 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

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]

Aris Marcolongo, Mykhailo Vladymyrov, Sebastian Lienert, Nadav Peleg, Sigve Haug, and 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, and 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, and Johannes Gerritzen. OmniOpt – A tool for hyperparameter optimization on HPC. In Heike Jagode, Hartwig Anzt, Hatem Ltaief, and Piotr Luszczek (Eds.), High Performance Computing - ISC High Performance Digital 2021 International Workshops, 2021, Revised Selected Papers, 285--296, Springer, Berlin u. a., Germany, Nov 13, 2021. [PUMA: FIS_scads High Hyperparameter Neural computing, networks optimization, performance]

Nishant Kumar, Lukas Krause, Thomas Wondrak, Sven Eckert, Kerstin Eckert, and 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, and 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