Biblio

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A
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.
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.
Constantiniu, A., Steinmann P., Bobach T., Farin G., & Umlauf G. (2008).  The adaptive Delaunay tesselation: A neighborhood covering meshing technique. Computational Mechanics. 42, 655-669.PDF icon AdaptDelTess.pdf (1.33 MB)
Franz, M. O., Schölkopf B., Mallot H. A., & Bülthoff H. H. (1996).  Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten. (Thielscher, M., & Bornscheuer S.-E., Ed.).Fortschritte der {Künstlichen Intelligenz}. 92.PDF icon Franz et al._1996_Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten.pdf (63.1 KB)
Seepold, R., Dermati C., Kostka A., Pfeil L., Lange R., Hermann M., et al. (2016).  Analyzing environmental conditions and vital signs to increase healthy living. Mobile Networks for Biometric Data Analysis.
Stadelmann, T., Stockinger K., Braschler M., Cieliebak M., Baudinot G., Dürr O., et al. (2013).  Applied Data Science in Europe: Challenges for Academia in Keeping Up with a Highly Demanded Topic. European Computer Science Summit. Amsterdam, Netherlands.
Stadelmann, T., Stockinger K., Braschler M., Cieliebak M., Baudinot G., Dürr O., et al. (2013).  Applied Data Science in Europe: Challenges for Academia in Keeping Up with a Highly Demanded Topic. European Computer Science Summit. Amsterdam, Netherlands.
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.
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
Yovel, Y., Stilz P., Melcón M. L., Franz M. O., & Schnitzler H.-U. (2008).  Bats can use echolocation calls for individual recognition. {Proc. Sensory coding and the natural environment 2008}.
Yovel, Y., Stilz P., Melcón M. L., Franz M. O., & Schnitzler H.-U. (2008).  Bats can use echolocation calls for individual recognition. {Proc. Sensory coding and the natural environment 2008}.
Stadelmann, T., Tolkachev V., Sick B., & Dürr O. (2019).  Beyond ImageNet: Deep Learning in Industrial Practice. Applied Data Science. 205-232.
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
Stadelmann, T., Glinski-Haefeli S., Gerber P., & Dürr O. (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)
Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2007).  Center-surround filters emerge from optimizing predictivity in a free-viewing task. {Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
Kienzle, W., Franz M. O., & Schölkopf B. (2009).  Center-surround patterns emerge as optimal predictors for human saccade targets. J. of Vision. 9, 1–15.PDF icon Kienzle, Franz, Schölkopf_2009_Center-surround patterns emerge as optimal predictors for human saccade targets.pdf (900.5 KB)
Schuldt, T., Döringshoff K., Stühler J., Kovalchuk E., Franz M. O., Gohlke M., et al. (2013).  A compact high-performance frequency reference for space applications. {29th Intl. Symposium on Space Technology and Science (ISTS 2013), Nagoya (Japan)}. PDF icon Schuldt et al._2013_A Compact High-Performance Frequency Reference for Space Applications.pdf (369.85 KB)
Schuldt, T., Döringshoff K., Stühler J., Kovalchuk E., Franz M. O., Gohlke M., et al. (2013).  A compact high-performance frequency reference for space applications. {29th Intl. Symposium on Space Technology and Science (ISTS 2013), Nagoya (Japan)}. PDF icon Schuldt et al._2013_A Compact High-Performance Frequency Reference for Space Applications.pdf (369.85 KB)
Ginkel, I., & Umlauf G. (2006).  Controlling a subdivision tuning method. (Cohen, A., Merrien J.-L., & Schumaker L.L., Ed.).Curve and Surface Fitting. PDF icon SubTuning.pdf (553.79 KB)
D
Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2020).  Deep and interpretable regression models for ordinal outcomes. arXiv preprint. 2010.08376.
Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2022).  Deep and interpretable regression models for ordinal outcomes. Pattern Recognition. 122, 108263.
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.
Schuldt, T., Schubert C., Krutzik M., Bote L., Gaaloul N., Hartwig J., et al. (2015).  Design of a dual species atom interferometer for space. Experimental Astronomy. 39, 167-206.PDF icon Schuldt et al._2015_Design of a dual species atom interferometer for space.pdf (2.98 MB)

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