Biblio

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1
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.
A
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.
Casanova, R., Murina E., Haberecker M., Honcharova-Biletska H., Vrugt B., Dürr O., et al. (2018).  Automatic classification of non-small cell lung cancer histologic sub-types by deep learning. VIRCHOWS ARCHIV. 108-108.
B
Dürr, O., Fan P-Y., & Yin Z-X. (2023).  Bayesian Calibration of MEMS Accelerometers. IEEE Sensors Journal.
Stadelmann, T., Tolkachev V., Sick B., & Dürr O. (2019).  Beyond ImageNet: Deep Learning in Industrial Practice. Applied Data Science. 205-232.
Rohrbach, J., Rainhard T., Sick B., & Dürr O. (2019).  Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks.
C
[Anonymous] (2018).  Capturing Suprasegmental Features of a Voice with RNNs for Improved Speaker Clustering. IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 333–345.PDF icon ANNPR_2018b.pdf (692.47 KB)
D
Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2022).  Deep and interpretable regression models for ordinal outcomes. Pattern Recognition. 122, 108263.
[Anonymous] (2015).  Deep Learning on a Raspberry Pi for Real Time Face Recognition.. Eurographics (Posters). 11–12.
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.
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.
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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[Anonymous] (2002).  Effective medium theory of conduction in stretched polymer electrolytes. arXiv preprint cond-mat/0202165.
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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.
I
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.
J
[Anonymous] (2014).  JOINT\_FORCES: Unite Competing Sentiment Classifiers with Random Forest.. SemEval@ COLING. 366–369.
K
[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.PDF icon adt.2018.859.pdf (711.06 KB)
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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.PDF icon MLSP_2017.pdf (1.34 MB)
[Anonymous] (2018).  Learning Neural Models for End-to-End Clustering. IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 126–138.PDF icon ANNPR_2018a.pdf (3.43 MB)
M
[Anonymous] (2014).  Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools.. Language Resources and Evaluation Conference (LREC). 3100–3104.
[Anonymous] (1998).  Monte-carlo-simulationen zu polymeren ionenleitern.
N
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.
P
[Anonymous] (2013).  Potential and Limitations of Commercial Sentiment Detection Tools.. ESSEM@ AI* IA. 47–58.
Dürr, O., Sick B., & Murina E. (2020).  Probabilistic deep learning: With python, keras and tensorflow probability.
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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