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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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[Anonymous] (2012).  Using Community Structure for Complex Network Layout. arXiv preprint arXiv:1207.6282.

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Arpogaus, M., Voss M., Sick B., Nigge-Uricher M., & Dürr O. (2023).  Short-term density forecasting of low-voltage load using Bernstein-polynomial normalizing flows. IEEE Transactions on Smart Grid.

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[Anonymous] (2007).  Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay. Journal of biomolecular screening. 12, 1042–1049.

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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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[Anonymous] (1998).  Monte-carlo-simulationen zu polymeren ionenleitern.

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[Anonymous] (2018).  Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes. Assay and drug development technologies. 16, 343–349.

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Herzog, L., Murina E., Dürr O., Wegener S., & Sick B. (2020).  Integrating uncertainty in deep neural networks for MRI based stroke analysis. Medical Image Analysis. 65, 101790.

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[Anonymous] (2015).  Gene expression signatures predictive of bevacizumab/erlotinib therapeutic benefit in advanced non-squamous non-small cell lung cancer patients (SAKK 19/05 trial). Clinical Cancer Research. clincanres––3135.

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[Anonymous] (2002).  Effective medium theory of conduction in stretched polymer electrolytes. arXiv preprint cond-mat/0202165.

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

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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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Rohrbach, J., Rainhard T., Sick B., & Dürr O. (2019).  Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks.

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[Anonymous] (2016).  Speaker Identification and Clustering using Convolution Neural Networks. IEEE International workshop on Machine Learning for Signal Processing.

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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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[Anonymous] (2013).  Potential and Limitations of Commercial Sentiment Detection Tools.. ESSEM@ AI* IA. 47–58.

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[Anonymous] (2014).  Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools.. Language Resources and Evaluation Conference (LREC). 3100–3104.

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[Anonymous] (2018).  Learning Neural Models for End-to-End Clustering. IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 126–138.

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[Anonymous] (2017).  Learning embeddings for speaker clustering based on voice equality. Machine Learning for Signal Processing (MLSP), 2017 IEEE 27th International Workshop on. 1–6.

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MLSP_2017.pdf (1.34 MB)