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Dold, D., Arpogaus M., & Dürr O. (2023).  Deep probabilistic modelling for energy forecasting.

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Poster_Deep probabilistic modelling for energy forecasting TTT.pdf (839.27 KB)

Herzog, L., Kook L., Götschi A., Petermann K., Hänsel M., Hamann J., et al. (2023).  Deep transformation models for functional outcome prediction after acute ischemic stroke. Biometrical Journal. 65, 2100379.

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Bodai, T., Lembo V., Lembo V., Lee S-S., Ishizu M., & Franz M. (2023).  Development and application of a climate emulator. EGU23.

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Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2022).  Deep and interpretable regression models for ordinal outcomes. Pattern Recognition. 122, 108263.

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Hermann, M., Dold D., Umlauf G., & Dürr O. (2022).  DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency. 2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning.

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AHM2022_DeepDoubt.pdf (238.98 KB)

Kibii, J. E., Dreher A., Wormser P. L., & Gimpel H. (2022).  Design and Calibration of Plane Mirror Setups for Mobile Robots with a 2D-Lidar. Sensors. 22, 7830.

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Sick, B., Hothorn T., & Dürr O. (2021).  Deep transformation models: Tackling complex regression problems with neural network based transformation models. Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021.

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Hake, F., Hermann M., Alkhatib H., Hesse C., Holste K., Umlauf G., et al. (2020).  Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms. FIG Working Week 2020.

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Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2020).  Deep and interpretable regression models for ordinal outcomes. arXiv preprint. 2010.08376.

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Pearse, G. D., Tan A. Y. S., Watt M. S., Franz M. O., & Dash J. P. (2020).  Detecting and mapping tree seedlings in UAV imagery using convolutional neural networks and field-verified data. ISPRS Journal of Photogrammetry and Remote Sensing. 168, 156 - 169.

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Schall, M., Schambach M-P., & Franz M. O. (2019).  Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification. 15th IAPR International Conference on Document Analysis and Recognition.

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Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification.pdf (553.24 KB)

Laube, P., Franz M. O., & Umlauf G. (2018).  Deep Learning Parametrization for B-Spline Curve Approximation. 2018 International Conference on 3D Vision (3DV). 691–699.

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0109.pdf (675.91 KB)

Siegismund, D., Tolkachev V., Heyse S., Sick B., Dürr O., & Steigele S. (2018).  Developing deep learning applications for life science and pharma industry. Drug research. 68, 305–310.

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Hermann, M., Madrid N., & Seepold R. (2017).  Detection of variations in holter ECG recordings based on dynamic cluster analysis. International Conference on Intelligent Decision Technologies.

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Bobach, T., Farin G., Hansford D., & Umlauf G. (2007).  Discrete harmonic functions from local coordinates. (Martin, R., Sabin M., & Winkler J., Ed.).Mathematics of Surfaces XII.

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HarmonicFunc.pdf (835.96 KB)