Publications

Niklas Deckers, and Martin Potthast. WARC-DL: Scalable Web Archive Processing for Deep Learning. 2022. [PUMA: Archive WARC-DL Web Learning Scalable Deep Processing] URL

Fabian Hart, Martin Waltz, and Ostap Okhrin. Two-step dynamic obstacle avoidance. Knowledge-based systems, (302)Elsevier Science B.V., Oct 25, 2024. [PUMA: FIS_scads Dynamic Local learning Deep Supervised planning, path learning, avoidance, reinforcement topic_engineering obstacle]

Simon M. Hofmann, Frauke Beyer, Sebastian Lapuschkin, Ole Goltermann, Markus Loeffler, Klaus-Robert Müller, Arno Villringer, Wojciech Samek, and A. Veronica Witte. Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain. NeuroImage, (261):119504, 2022. [PUMA: Cardiovascular deep Brain-age, Structural learning Ageing, a.i., mri, Explainable topic_lifescience topic_neuroinspired risk factors,] URL

Martin Waltz, Ostap Okhrin, and Michael Schultz. Self-organized free-flight arrival for urban air mobility. Transportation Research Part C: Emerging Technologies, (167):104806, 2024. [PUMA: Urban mobility eVTOL air learning reinforcement Deep topic_engineering] URL

Maksim Kukushkin, Martin Bogdan, and Thomas Schmid. On optimizing morphological neural networks for hyperspectral image classification. In Wolfgang Osten (Eds.), Sixteenth International Conference on Machine Vision (ICMV 2023), (13072):1307202, SPIE, 2024. [PUMA: image sensing morphological morphology deep neuronal learning networks classification remote mathematical computer hyperspectral vision] URL

Vincent D. Friedrich, Peter Pennitz, Emanuel Wyler, Julia M. Adler, Dylan Postmus, Kristina Müller, Luiz Gustavo Teixeira Alves, Julia Prigann, Fabian Pott, Daria Vladimirova, Thomas Hoefler, Cengiz Goekeri, Markus Landthaler, Christine Goffinet, Antoine-Emmanuel Saliba, Markus Scholz, Martin Witzenrath, Jakob Trimpert, Holger Kirsten, and Geraldine Nouailles. Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease. eBioMedicine, (108):105312, 2024. [PUMA: Disease Cross-species model, learning Deep matching, Single-cell RNA-seq, topic_mathfoundation COVID-19, analysis, state Hamster] 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: Mediterranean Extreme Learning Wildfires Spatiotemporal Trends Statistical Deep] URL

Ravi Pradip, and Frank Cichos. Deep reinforcement learning with artificial microswimmers. Emerging Topics in Artificial Intelligence (ETAI) 2022, (12204):104--110, 2022. [PUMA: microswimmers artificial learning topic_physchemistry reinforcement Deep]

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: deep RNA non-coding feature learning extraction BioDeepfuse classification] URL

Narmin Ghaffari Laleh, Hannah Sophie Muti, Chiara Maria Lavinia Loeffler, Amelie Echle, Oliver Lester Saldanha, Faisal Mahmood, Ming Y Lu, Christian Trautwein, Rupert Langer, Bastian Dislich, Roman D Buelow, Heike Irmgard Grabsch, Hermann Brenner, Jenny Chang-Claude, Elizabeth Alwers, Titus J Brinker, Firas Khader, Daniel Truhn, Nadine T Gaisa, Peter Boor, Michael Hoffmeister, Volkmar Schulz, and Jakob Nikolas Kather. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal., (79)102474:102474, Elsevier BV, July 2022. [PUMA: Convolutional Artificial intelligence; Multiple-Instance deep neural networks; learning Weakly-supervised Learning; transformers; pathology; topic_lifescience Vision Computational]

Dianzhao Li, and Ostap Okhrin. A platform-agnostic deep reinforcement learning framework for effective Sim2Real transfer towards autonomous driving. Commun Eng, (3)1:147, Springer Science and Business Media LLC, October 2024. [PUMA: driving framework platform-agnostic autonomous deep learning reinforcement Sim2Real]