Publications

Anna Willmann, Jurjen Couperus Cabadağ, Yen-Yu Chang, Richard Pausch, Amin Ghaith, Alexander Debus, Arie Irman, Michael Bussmann, Ulrich Schramm, and Nico Hoffmann. Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows. 2023. [PUMA: Bunch Distribution Electron Learning Zno imported] URL

Patrick Stiller, Varun Makdani, Franz Pöschel, Richard Pausch, Alexander Debus, Michael Bussmann, and Nico Hoffmann. Continual learning autoencoder training for a particle-in-cell simulation via streaming. 2022. [PUMA: Continual Zno autoencoder imported learning particle-in-cell simulation training] URL

Milad Alshomary, Timon Gurcke, Shahbaz Syed, Philipp Heinisch, Maximilian Spliethöver, Philipp Cimiano, Martin Potthast, and Henning Wachsmuth. Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. In Khalid Al-Khatib, Yufang Hou, and Manfred Stede (Eds.), 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP, 184--189, Association for Computational Linguistics, November 2021. [PUMA: Analysis Argument Contrastive Extractive Key Learning Point Summarization Zno]

Sascha Marton, Stefan Lüdtke, Christian Bartelt, and Heiner Stuckenschmidt. GradTree: Learning axis-aligned decision trees with gradient descent. 2023. [PUMA: GradTree Learning Zno axis-aligned decision descent gradient trees]

Ricardo Knauer, and Erik Rodner. Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective. KI 2023: Advances in Artificial Intelligence: 46th German Conference on AI, Berlin, Germany, September 26--29, 2023, Proceedings, 114--129, Springer-Verlag, Berlin, Heidelberg, 2023. [PUMA: Zno best conic cost-sensitive interpretable learning machine meta-learning mixed-integer optimization selection subset]

Sophie Adama, Shang-Ju Wu, Nicoletta Nicolaou, and Martin Bogdan. Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning. SNE Simul. Notes Eur., (32)1:37--45, ARGESIM Arbeitsgemeinschaft Simulation News, 2022. [PUMA: Zno conscious hybrid learning machine patient states {CLIS}]

Oliver Kirsten, Martin Bogdan, and Sophie Adama. Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), 37-41, 2023. [PUMA: Complexity Computational Consciousness Information Learning Locked-In Machine Measures Modeling Neuroscience Prediction Predictive Processing Signal Syndrome Theory Training Zno algorithms and data learning modeling models processing]

Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, and André C.P.L.F. de Carvalho. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biology, (21)1:410–421, Informa UK Limited, March 2024. [PUMA: BioDeepfuse RNA Zno classification deep extraction feature learning non-coding] URL

Jordan Richards, Raphaël Huser, Emanuele Bevacqua, and Jakob Zscheischler. Insights into the Drivers and Spatiotemporal Trends of Extreme Mediterranean Wildfires with Statistical Deep Learning. Artificial Intelligence for the Earth Systems, (2)4American Meteorological Society, October 2023. [PUMA: Deep Extreme Learning Mediterranean Spatiotemporal Statistical Trends Wildfires zno] URL

Lummy Maria Monteiro, Joao Saraiva, Rodolfo Toscan, Peter Stadler, Rafael Silva-Rocha, and Ulisses Nunes da Rocha. PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning. Environmental Microbiome, (17)December 2022. [PUMA: PredicTF: Zno bacterial complex learning microbialdeep prediction transcription]