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Showing results 1 to 8 of 8
Issue DateTitleInvolved Person(s)
9-Jun-2021A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applicationsSimmler, Niclas; Sager, Pascal; Andermatt, Philipp; Chavarriaga, Ricardo; Schilling, Frank-Peter, et al
Sep-2020Artificial neural networks in pattern recognition : proceedings of the 9th IAPR TC3 workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020Schilling, Frank-Peter; Stadelmann, Thilo
15-May-2019Deep Learning in medizinischer Diagnostik und QualitätskontrolleStadelmann, Thilo; Schilling, Frank-Peter
9-Jun-2022Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCTHerzig, Ivo; Paysan, Pascal; Scheib, Stefan; Züst, Alexander; Schilling, Frank-Peter, et al
6-Nov-2020Design patterns for resource-constrained automated deep-learning methodsTuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar, et al
2019Efficient deep CNNs for cross-modal automated computer vision under time and space constraintsAmirian, Mohammadreza; Rombach, Katharina; Tuggener, Lukas; Schilling, Frank-Peter; Stadelmann, Thilo
13-May-2022Foundations of Data Science : a comprehensive overview formed at the 1st International Symposium on the Science of Data ScienceSchilling, Frank-Peter; Flumini, Dandolo; Füchslin, Rudolf Marcel; Gavagnin, Elena; Geller, Armando, et al
Mar-2021Two to trust : AutoML for safe modelling and interpretable deep learning for robustnessAmirian, Mohammadreza; Tuggener, Lukas; Chavarriaga, Ricardo; Satyawan, Yvan Putra; Schilling, Frank-Peter, et al