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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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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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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Scharpf, P., Hong C. Lap, & Dürr O. (2021).  Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations. 2021 International Conference on Data Mining Workshops (ICDMW). 398–404.

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Scharpf, P., Hong C. Lap, & Duerr O. (2021).  Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations. 2021 International Conference on Data Mining Workshops (ICDMW). 398-404.

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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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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)

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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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)

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)

Berlin, C., Adomeit S., Grover P., Dreischarf M., Dürr O., & Obid P. (2022).  140. Automated measurement technique for coronal parameters using a novel artificial intelligence algorithm: an independent validation study on 100 preoperative AP spine X-rays. The Spine Journal. 22, S74.

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Berlin, C., Adomeit S., Grover P., Dreischarf M., Dürr O., & Obid P. (2022).  140. Automated measurement technique for coronal parameters using a novel artificial intelligence algorithm: an independent validation study on 100 preoperative AP spine X-rays. The Spine Journal. 22, S74.

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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)

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)

Hermann, M., Griesser D., Gundel B., Dold D., Umlauf G., & Franz M. O. (2022).  Targetless Lidar-camera registration using patch-wise mutual information. International Conference on Information Fusion.

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mir_reg_patch.pdf (9.58 MB)

Adomeit, S., Berlin C., Grover P., Dreischarf M., Halm H., Dürr O., et al. (2022).  Validation study of an algorithm based on artificial intelligence for automated computation of coronal parameters on preoperative AP X-rays. Brain and Spine. 2, 101156.

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Adomeit, S., Berlin C., Grover P., Dreischarf M., Halm H., Dürr O., et al. (2022).  Validation study of an algorithm based on artificial intelligence for automated computation of coronal parameters on preoperative AP X-rays. Brain and Spine. 2, 101156.

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Dürr, O., Fan P-Y., & Yin Z-X. (2023).  Bayesian Calibration of MEMS Accelerometers. IEEE Sensors Journal.

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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)

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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Hoher, P., Reuter J., Dold D., Griesser D., Govaers F., & Koch W. (2023).  Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model. International Conference on Information Fusion (FUSION). 1-8.

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Berlin, C., Adomeit S., Grover P., Dreischarf M., Halm H., Dürr O., et al. (2023).  Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays. Global Spine Journal. 21925682231154543.

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