Publications

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

Dimitra Kiakou, Adam Adamopoulos, and Nico Scherf. Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection. Worldwide Congress on “Genetics, Geriatrics and Neurodegenerative Diseases Research", 223--230, 2022. [PUMA: Disease Feature Graph-Based Impact Investigating Neuroimaging Prediction Selection learning]

Lars Muschalski, Joanna Wollmann, Andreas Hornig, and Niels Modler. Steuerung von Compliant-Mechanismen durch Reinforcement Learning. GETRIEBETAGUNG 2022, 121, 2022. [PUMA: Compliant-Mechanismen Learning Reinforcement Steuerung]

Johannes Gerritzen, Andreas Hornig, Peter Winkler, and Maik Gude. A methodology for direct parameter identification for experimental results using machine learning—Real world application to the highly non-linear deformation behavior of FRP. Computational Materials Science, (244):113274, Elsevier, 2024. [PUMA: FRP Real application behavior deformation direct experimental highly identification learning machine non-linear parameter results using world]

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: Deep Urban air eVTOL learning mobility reinforcement] URL

Fabian Hart, and Ostap Okhrin. Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk. Neurocomputing, (568):127097, 2024. [PUMA: Collision Dynamic Reinforcement Training avoidance environment learning metric obstacle risk] URL

Dianzhao Li, and Ostap Okhrin. Vision-Based DRL Autonomous Driving Agent with Sim2Real Transfer. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 866-873, 2023. [PUMA: Automobiles Autonomous Measurement Reinforcement Statistical Task Videos analysis learning vehicles]

Fabian Hart, Ostap Okhrin, and Martin Treiber. Vessel-following model for inland waterways based on deep reinforcement learning. Ocean Eng., (281)114679:114679, Elsevier BV, August 2023. [PUMA: Vessel-following deep inland learning model reinforcement waterways]

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: COVID-19, Cross-species Deep Disease Hamster RNA-seq, Single-cell analysis, learning matching, model, state] URL

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, and Christian L. Müller. Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences,] URL

Maik Fröbe, Janek Bevendorff, Jan Heinrich Reimer, Martin Potthast, and Matthias Hagen. Sampling Bias Due to Near-Duplicates in Learning to Rank. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 1997–2000, Association for Computing Machinery, New York, NY, USA, 2020. [PUMA: bias learning near-duplicate-detection, novelty principle, rank, selection to] URL

Jana Riedel, and Julia Kleppsch. Wie bereit sind Studierende für die Nutzung von KI-Technologien? Eine Annäherung an die KI-Readiness Studierender im Kontext des Projektes "tech4comp". Waxmann : Münster ; New York, 2021. [PUMA: (Learning 370 Activities), Artificial Assessment, Bewertung, Bildungswesen, Deployment Digitale Education, Empirical Empirische Erziehung, Forschung, Higher Hochschule, Hochschullehre, Human Intelligenz, Judgement, Judgment, K\"{u}nstliche Learning Lernprozess, Male Medien, Medieneinsatz, Mediennutzung, Mensch, Nachteil, Project, Projects Projekt, Qualitative Schul- Student, Technologie, Technology, University Untersuchung, Use Utilisation Utilization Vergleich, Vorteil, being, education institute, intelligence, lecturing, media, of process, research student, study, teaching, und] URL

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: Ageing, Brain-age, Cardiovascular Explainable Structural a.i., deep factors, learning mri, risk] URL

Christopher Klapproth, Rituparno Sen, Peter F Stadler, Sven Findeiß, and Jörg Fallmann. Common features in lncRNA annotation and classification: A survey. Noncoding RNA, (7)4:77, MDPI AG, December 2021. [PUMA: classification coding extraction; feature learning lncRNA; machine problems; sequence;]

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: Artificial Computational Convolutional Learning; Multiple-Instance Vision Weakly-supervised deep intelligence; learning networks; neural pathology; transformers;]

Marie Steinacker, Yuri Kheifetz, and Markus Scholz. Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach. Heliyon, (9)7:e17890, Elsevier BV, July 2023. [PUMA: Haematopoiesis; Precision Recurrent System Transfer identification; learning medicine; networks; neural unit_transfer]