Publications

Anna Willmann, Jurjen Couperus Cabadağ, Yen-Yu Chang, Richard Pausch, Amin Ghaith, Alexander Debus, Arie Irman, Michael Bussmann, Ulrich Schramm, und 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, und 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, und Henning Wachsmuth. Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. In Khalid Al-Khatib, Yufang Hou, und Manfred Stede (Hrsg.), 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]

Saeed Karami, Farid Saberi-Movahed, Prayag Tiwari, Pekka Marttinen, und Sahar Vahdati. Unsupervised feature selection based on variance–covariance subspace distance. Neural Networks, (166):188-203, 2023. [PUMA: Feature Regularization Subspace Xack distance learning selection] URL

Markus Bauer, und Christoph Augenstein. Self-supervised learning in histopathology: New perspectives for prostate cancer grading. Lecture Notes in Computer Science, 348--360, Springer Nature Switzerland, Cham, 2024. [PUMA: Yaff cancer grading histopathology learning prostate self-supervised topic_lifescience]

Akshay Akshay, Masoud Abedi, Navid Shekarchizadeh, Fiona C Burkhard, Mitali Katoch, Alex Bigger-Allen, Rosalyn M Adam, Katia Monastyrskaya, und Ali Hashemi Gheinani. MLcps: machine learning cumulative performance score for classification problems. GigaScience, (12):giad108, Dezember 2023. [PUMA: MLcps Xack Yaff cumulative learning machine performance] URL

Parvaneh Joharinad, und Jürgen Jost. Manifold learning, the scheme of Laplacian eigenmaps. Mathematics of Data, 227--251, Springer International Publishing, Cham, 2023. [PUMA: Laplacian Manifold eigenmaps learning nopdf]

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

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

Ricardo Knauer, und 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]

Dianzhao Li, und 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, Oktober 2024. [PUMA: Sim2Real Xack autonomous deep driving framework learning platform-agnostic reinforcement]

Sunna Torge, Waldemar Hahn, Lalith Manjunath, und René Jäkel. Named Entity Recognition for Specific Domains - Take Advantage of Transfer Learning. International Journal of Information Science and Technology, Vol 6 No 3 (2022), International Journal of Information Science and Technology, 2022. [PUMA: Advantage Domains Entity Learning Recognition Specific Transfer Xack] URL

Sophie Adama, Shang-Ju Wu, Nicoletta Nicolaou, und 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}]

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]

Oliver Kirsten, Martin Bogdan, und 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]

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 nopdf remote sensing vision] URL

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, und 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, März 2024. [PUMA: BioDeepfuse RNA Zno classification deep extraction feature learning non-coding] URL

Meysam Alishahi, Anna Little, und Jeff M. Phillips. Linear Distance Metric Learning with Noisy Labels. Journal of Machine Learning Research, (25)121:1--53, 2024. [PUMA: Distance Learning Linear Metric Noisy_Labels Yaff imported] URL

Jordan Richards, Raphaël Huser, Emanuele Bevacqua, und 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, Oktober 2023. [PUMA: Deep Extreme Learning Mediterranean Spatiotemporal Statistical Trends Wildfires zno] URL

Aruscha Kramm, Eric Peukert, André Ludwig, und Bogdan Franczyk. Machine Learning Based Mobile Capacity Estimation for Roadside Parking. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (10):99--106, Copernicus GmbH, 2024. [PUMA: Based Capacity Estimation Learning Machine Mobile Parking Roadside yaff]

Maria Carolina Novitasari, Johannes Quaas, und Miguel R. D. Rodrigues. Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning. Environmental Data Science, (3)Cambridge University Press (CUP), 2024. [PUMA: autoconversion forecasting learning machine rates satellite yaff] URL

Tom Richard Vargis, und Siavash Ghiasvand. A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement. The International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, Texas, USA, Januar 2022. [PUMA: Cluster Computer Computing, Distributed, Learning Machine Parallel, Science and myOwn] URL

Markus Bauer, und Christoph Augenstein. Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.. Proceedings of the 18th Conference on Computer Science and Intelligence Systems, (35):93–101, IEEE, September 2023. [PUMA: Comparative Computer Data Learning Self-Supervised Study Unlabelled Vision yaff] URL

Oscar J. Pellicer-Valero, Miguel-Ángel Fernández-Torres, Chaonan Ji, Miguel D. Mahecha, und Gustau Camps-Valls. Explainable Earth Surface Forecasting under Extreme Events. arXiv, 2024. [PUMA: (cs.LG), Computer FOS: Learning Machine and information sciences sciences, topic_earthenvironment] URL

Lucas Lange, Maurice-Maximilian Heykeroth, und Erhard Rahm. Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning. arXiv preprint arXiv:2409.01329, arXiv, September 2024. [PUMA: area_responsibleai area_bigdata (cs.CR), (cs.CV), (cs.LG), Computer Cryptography FOS: Learning Machine Pattern Recognition Security Vision and ep information sciences]

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

Dimitra Kiakou, Adam Adamopoulos, und 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: topic_neuroinspired Disease Feature Graph-Based Impact Investigating Neuroimaging Prediction Selection learning]

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

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

Fabian Hart, und Ostap Okhrin. Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk. Neurocomputing, (568):127097, 2024. [PUMA: topic_engineering Collision Dynamic Reinforcement Training avoidance environment learning metric obstacle risk] 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, und Geraldine Nouailles. Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease. eBioMedicine, (108):105312, 2024. [PUMA: topic_mathfoundation 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, und 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

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

Simon M. Hofmann, Frauke Beyer, Sebastian Lapuschkin, Ole Goltermann, Markus Loeffler, Klaus-Robert Müller, Arno Villringer, Wojciech Samek, und 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: topic_neuroinspired topic_lifescience Ageing, Brain-age, Cardiovascular Explainable Structural a.i., deep factors, learning mri, risk] URL

Maik Fröbe, Janek Bevendorff, Jan Heinrich Reimer, Martin Potthast, und 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, und 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

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, und Jakob Nikolas Kather. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal., (79)102474:102474, Elsevier BV, Juli 2022. [PUMA: topic_lifescience Artificial Computational Convolutional Learning; Multiple-Instance Vision Weakly-supervised deep intelligence; learning networks; neural pathology; transformers;]

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

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