Arpogaus, M., Voß M., Sick B., Nigge-Uricher M., & Dürr O. (2021).  Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows. ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual.

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Arpogaus, M., Voß M., Sick B., Nigge-Uricher M., & Dürr O. (2021).  Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows. ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual.

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Arpogaus2021_Probabilistic_Forecasting.pdf (427.35 KB)

Dürr, O., Sick B., & Murina E. (2020).  Probabilistic deep learning: With python, keras and tensorflow probability.

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Dürr, O., Sick B., & Murina E. (2020).  Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability.

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Bohnet, D., & Bonatti C. (2016).  Partially hyperbolic diffeomorphisms with uniformly compact center foliation: quotient dynamics. Ergodic Theory Dyn. Sys.. 36(4), 

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Grunwald, M., Müller J., Schall M., Laube P., Umlauf G., & Franz M. O. (2015).  Pixel-wise Hybrid Image Registration on Wood Decors. BW-CAR| SINCOM. 24.

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Grunwald_2015_Pixel-wiseHybridImageRegistration.pdf (2.42 MB)

Cieliebak, M., Dürr O., & Uzdilli F. (2013).  Potential and Limitations of Commercial Sentiment Detection Tools.. ESSEM@ AI* IA. 47–58.

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Yovel, Y., Franz M. O., Stilz P., & Schnitzler H.-U. (2008).  Plant classification from bat-like echolocation signals. PLoS Comput.\ Biol.. 4, e1000032.

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Yovel et al._2008_Plant classification from bat-like echolocation signals.pdf (772.54 KB)

Prautzsch, H., & Umlauf G. (2006).  Parametrizations for triangular G^k spline surfaces of low degree. ACM Transactions on Graphics. 24, 1281-1293.

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GkSplineSurf.pdf (539.25 KB)

Dieterich, W., Dürr O., Pendzig P., Bunde A., & Nitzan A. (1999).  Percolation concepts in solid state ionics. Physica A: Statistical Mechanics and its Applications. 266, 229–237.

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