Semi-Supervised Learning for Grain Size Distribution Interpolation. - Konstantin Kobs; Christian Schäfer; Michael Steininger; Anna Krause; Roland Baumhauer; Heiko Paeth; Andreas Hotho (ICPR Workshops (6), 2020)

SimLoss: Class Similarities in Cross Entropy. - Konstantin Kobs; Michael Steininger; Albin Zehe; Florian Lautenschlager 002; Andreas Hotho (ISMIS, 2020)

Anomaly Detection in Beehives using Deep Recurrent Autoencoders. - Padraig Davidson; Michael Steininger; Florian Lautenschlager 002; Konstantin Kobs; Anna Krause; Andreas Hotho (SENSORNETS, 2020)

Deep Learning for Climate Model Output Statistics. - Michael Steininger; Daniel Abel; Katrin Ziegler; Anna Krause; Heiko Paeth; Andreas Hotho (CoRR, 2020)

MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images. - Michael Steininger; Konstantin Kobs; Albin Zehe; Florian Lautenschlager 002; Martin Becker 003; Andreas Hotho (ACM Trans. Spatial Algorithms Syst., 2020)

Detecting Presence Of Speech In Acoustic Data Obtained From Beehives. - Pascal Janetzky; Padraig Davidson; Michael Steininger; Anna Krause; Andreas Hotho (DCASE, 2021)

Do Different Deep Metric Learning Losses Lead to Similar Learned Features? - Konstantin Kobs; Michael Steininger; Andrzej Dulny; Andreas Hotho (ICCV, 2021)

Semi-unsupervised Learning: An In-depth Parameter Analysis. - Padraig Davidson; Florian Buckermann; Michael Steininger; Anna Krause; Andreas Hotho (KI, 2021)

Anomaly Detection in Beehives: An Algorithm Comparison. - Padraig Davidson; Michael Steininger; Florian Lautenschlager 002; Anna Krause; Andreas Hotho (CoRR, 2021)

Density-based weighting for imbalanced regression. - Michael Steininger; Konstantin Kobs; Padraig Davidson; Anna Krause; Andreas Hotho (Mach. Learn., 2021)

Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites. - Simone Zepp; Uta Heiden; Martin Bachmann; Martin Wiesmeier; Michael Steininger; Bas van Wesemael (Remote. Sens., 2021)

Semi-unsupervised Learning for Time Series Classification. - Padraig Davidson; Michael Steininger; André Huhn; Anna Krause; Andreas Hotho (CoRR, 2022)

Räumliche Erfassung des organischen Kohlenstoffgehaltes von Böden einer landwirtschaftlichen Intensivregion aus Sentinel-2-Daten. - Lucas Wittstruck; Heike Gerighausen; Annelie Säurich; Markus Möller; Knut Hartmann; Michael Steininger; Simone Zepp; Thomas Jarmer 001 (GIL Jahrestagung, 2023)

Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks. - Melanie Schaller; Michael Steininger; Andrzej Dulny; Daniel Schlör; Andreas Hotho (LWDA, 2023)

InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images. - Konstantin Kobs; Michael Steininger; Andreas Hotho (WACV, 2023)

ConvMOS: climate model output statistics with deep learning. - Michael Steininger; Daniel Abel; Katrin Ziegler; Anna Krause; Heiko Paeth; Andreas Hotho (Data Min. Knowl. Discov., 2023)

Deep Learning for Geospatial Environmental Regression (Deep Learning für Regressionsmodelle mit georäumlichen Umweltdaten) - Michael Steininger (2023)