Meier, B. Bruno, Stadelmann T., & Dürr O. (2018).  Learning to Cluster.

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learning_to_cluster.pdf (1.82 MB)

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

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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)

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)

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)

Brach, K., Sick B., & Dürr O. (2020).  Single Shot MC Dropout Approximation. ICML Workshop on Uncertainty and Robustness in Deep Learning.

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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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Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2020).  Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes. arXiv e-prints. 2010.08376.

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Hörtling, S., Dold D., Dürr O., & Sick B. (2021).  Transformation models for flexible posteriors in variational bayes. arXiv preprint. 2106.00528.

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2106.00528.pdf (1.03 MB)