Biblio

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2021
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.
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. PDF icon Arpogaus2021_Probabilistic_Forecasting.pdf (427.35 KB)
2020
Dürr, O., Sick B., & Murina E. (2020).  Probabilistic deep learning: With python, keras and tensorflow probability.
Dürr, O., Sick B., & Murina E. (2020).  Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability.
2016
Bohnet, D., & Bonatti C. (2016).  Partially hyperbolic diffeomorphisms with uniformly compact center foliation: quotient dynamics. Ergodic Theory Dyn. Sys.. 36(4), 
2015
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.PDF icon Grunwald_2015_Pixel-wiseHybridImageRegistration.pdf (2.42 MB)
2006
Prautzsch, H., & Umlauf G. (2006).  Parametrizations for triangular G^k spline surfaces of low degree. ACM Transactions on Graphics. 24, 1281-1293.PDF icon GkSplineSurf.pdf (539.25 KB)