Publications

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: (cs.CR), (cs.CV), (cs.LG), Computer Cryptography FOS: Learning Machine Pattern Recognition Security Vision and area_bigdata ep information sciences]

Johannes Gerritzen, Andreas Hornig, Peter Winkler, und 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: topic_engineering area_architectures FRP using experimental deformation direct learning application identification world parameter machine Real results highly non-linear behavior]

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

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;]