Showing results 21 to 40 of 70
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| Issue Date | Title | Involved Person(s) |
| Aug-2014 | Data Science für Lehre, Forschung und Praxis | Stockinger, Kurt; Stadelmann, Thilo |
| 2016 | Data Scientist als Beruf | Stockinger, Kurt; Stadelmann, Thilo; Ruckstuhl, Andreas |
| 14-Jun-2019 | Data scientists | Stadelmann, Thilo; Stockinger, Kurt; Heinatz-Bürki, Gundula; Braschler, Martin |
| 2023 | Deep ensemble inverse model for image-based estimation of solar cell parameters | Battaglia, Mattia; Comi, Ennio; Stadelmann, Thilo; Hiestand, Roman; Ruhstaller, Beat, et al |
| 2023 | Deep learning for robust and explainable models in computer vision | Schwenker, Friedhelm; Stadelmann, Thilo; Jaggi, Martin; Amirian, Mohammadreza |
| 15-May-2019 | Deep Learning in medizinischer Diagnostik und Qualitätskontrolle | Stadelmann, Thilo; Schilling, Frank-Peter |
| 2018 | Deep learning in the wild | Stadelmann, Thilo; Amirian, Mohammadreza; Arabaci, Ismail; Arnold, Marek; Duivesteijn, Gilbert François, et al |
| 9-Jun-2022 | Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT | Herzig, Ivo; Paysan, Pascal; Scheib, Stefan; Züst, Alexander; Schilling, Frank-Peter, et al |
| 26-Mar-2024 | Deep neural networks for automatic speaker recognition do not learn supra-segmental temporal features | Neururer, Daniel; Dellwo, Volker; Stadelmann, Thilo |
| 2018 | Deep watershed detector for music object recognition | Tuggener, Lukas; Elezi, Ismail; Schmidhuber, Jürgen; Stadelmann, Thilo |
| 2018 | DeepScores : a dataset for segmentation, detection and classification of tiny objects | Tuggener, Lukas; Elezi, Ismail; Schmidhuber, Jürgen; Pelillo, Marcello; Stadelmann, Thilo |
| 20-Sep-2018 | DeepScores and Deep Watershed Detection : current state and open issues | Elezi, Ismail; Tuggener, Lukas; Pelillo, Marcello; Stadelmann, Thilo |
| 2-Sep-2020 | DeepScoresV2 | Tuggener, Lukas; Satyawan, Yvan Putra; Pacha, Alexander; Schmidhuber, Jürgen; Stadelmann, Thilo |
| 6-Nov-2020 | Design patterns for resource-constrained automated deep-learning methods | Tuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar, et al |
| 2019 | Efficient deep CNNs for cross-modal automated computer vision under time and space constraints | Amirian, Mohammadreza; Rombach, Katharina; Tuggener, Lukas; Schilling, Frank-Peter; Stadelmann, Thilo |
| 2022 | FormulaNet : a benchmark dataset for mathematical formula detection | Schmitt-Koopmann, Felix M.; Huang, Elaine M.; Hutter, Hans-Peter; Stadelmann, Thilo; Darvishy, Alireza |
| 13-May-2022 | Foundations of Data Science : a comprehensive overview formed at the 1st International Symposium on the Science of Data Science | Schilling, Frank-Peter; Flumini, Dandolo; Füchslin, Rudolf Marcel; Gavagnin, Elena; Geller, Armando, et al |
| Jun-2023 | From concept to implementation : the data-centric development process for AI in industry | Luley, Paul-Philipp; Deriu, Jan Milan; Yan, Peng; Schatte, Gerrit A.; Stadelmann, Thilo |
| 2017 | Fully convolutional neural networks for newspaper article segmentation | Meier, Benjamin; Stadelmann, Thilo; Stampfli, Jan; Arnold, Marek; Cieliebak, Mark |
| 2-Sep-2020 | How (not) to measure bias in face recognition networks | Glüge, Stefan; Amirian, Mohammadreza; Flumini, Dandolo; Stadelmann, Thilo |
