Michael Steininger

According to our database, Michael Steininger authored at least 17 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Deep Learning for Geospatial Environmental Regression (Deep Learning für Regressionsmodelle mit georäumlichen Umweltdaten)

Michael Steininger

PhD thesis, 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

InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images.

Konstantin Kobs, Michael Steininger, Andreas Hotho

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 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

Proceedings of the Lernen, 2023

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

Proceedings of the 43. GIL-Jahrestagung, 2023

2022

Semi-unsupervised Learning for Time Series Classification.

Padraig Davidson, Michael Steininger, André Huhn, Anna Krause, Andreas Hotho

CoRR, 2022

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

Density-based weighting for imbalanced regression.

Michael Steininger, Konstantin Kobs, Padraig Davidson, Anna Krause, Andreas Hotho

Mach. Learn., 2021

Anomaly Detection in Beehives: An Algorithm Comparison.

Padraig Davidson, Michael Steininger, Florian Lautenschlager, Anna Krause, Andreas Hotho

CoRR, 2021

Semi-unsupervised Learning: An In-depth Parameter Analysis.

Padraig Davidson, Florian Buckermann, Michael Steininger, Anna Krause, Andreas Hotho

Proceedings of the KI 2021: Advances in Artificial Intelligence - 44th German Conference on AI, Virtual Event, September 27, 2021

Do Different Deep Metric Learning Losses Lead to Similar Learned Features?

Konstantin Kobs, Michael Steininger, Andrzej Dulny, Andreas Hotho

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Detecting Presence Of Speech In Acoustic Data Obtained From Beehives.

Pascal Janetzky, Padraig Davidson, Michael Steininger, Anna Krause, Andreas Hotho

Proceedings of the 6th Workshop on Detection and Classification of Acoustic Scenes and Events 2021 (DCASE 2021), 2021

2020

MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images.

Michael Steininger, Konstantin Kobs, Albin Zehe, Florian Lautenschlager, Martin Becker, Andreas Hotho

ACM Trans. Spatial Algorithms Syst., 2020

Deep Learning for Climate Model Output Statistics.

Michael Steininger, Daniel Abel, Katrin Ziegler, Anna Krause, Heiko Paeth, Andreas Hotho

CoRR, 2020

Anomaly Detection in Beehives using Deep Recurrent Autoencoders.

Padraig Davidson, Michael Steininger, Florian Lautenschlager, Konstantin Kobs, Anna Krause, Andreas Hotho

Proceedings of the 9th International Conference on Sensor Networks, 2020

SimLoss: Class Similarities in Cross Entropy.

Konstantin Kobs, Michael Steininger, Albin Zehe, Florian Lautenschlager, Andreas Hotho

Proceedings of the Foundations of Intelligent Systems - 25th International Symposium, 2020

Semi-Supervised Learning for Grain Size Distribution Interpolation.

Konstantin Kobs, Christian Schäfer, Michael Steininger, Anna Krause, Roland Baumhauer, Heiko Paeth, Andreas Hotho

Proceedings of the Pattern Recognition. ICPR International Workshops and Challenges, 2020