Biblio

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Staedtgen, M., Hahn S., Franz M. O., & Spitzer M. (2000).  Subliminale Darbietung verkehrsrelevanter Information in Kraftfahrzeugen. (Bülthoff, H. H., Gegenfurtner K. R., & Mallot H. A., Ed.).{Proc. 3. Tübinger Wahrnehmungskonferenz (TWK 20009)}. 98.
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., Tolkachev V., Sick B., & Dürr O. (2019).  Beyond ImageNet: Deep Learning in Industrial Practice. Applied Data Science. 205-232.
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)
Sinz, F., J Candela Q., Bakır G. H., Rasmussen C. E., & Franz M. O. (2004).  Learning depth from stereo. (Rasmussen, C. E., Bülthoff H. H., Giese M. A., & Schölkopf B., Ed.).{Pattern Recognition, Proc.\ 26th DAGM Symposium}. 3175, 245 – 252.
Sinz, F., & Franz M. O. (2004).  Learning depth. (Bülthoff, H. H., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 7. Tübinger Wahrnehmungskonferenz (TWK 2004)}. 68.PDF icon Sinz et al Learning depth 2004.pdf (197 KB)
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.
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.
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.
Schwamberger, V., Le P. H. D., Schölkopf B., & Franz M. O. (2010).  The influence of the image basis on modeling and steganalysis performance. (Böhme, R., Fong P.W.L., & Safavi-Naini R., Ed.).{Proc. 12th Intl. Conf. on Information Hiding (IH-2010)}. 133–144.PDF icon Schwamberger et al._2010_The Influence of the Image Basis on Modeling and Steganalysis Performance.pdf (392.83 KB)
Schwamberger, V., & Franz M. O. (2010).  Simple algorithmic modifications for improving blind steganalysis performance. {Proc. of the 2010 Workshop on Multimedia and Security (MM&Sec 2010)}. PDF icon Schwamberger, Franz_2010_Simple Algorithmic Modifications for Improving Blind Steganalysis Performance.pdf (813.87 KB)
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)
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)
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.
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.
Schambach, M-P., von der Nüll S., & Schall M. (2019).  Fast and Reliable Acquisition of Truth Data for Document Analysis using Cyclic Suggest Algorithms. ICDAR-OST: The 2nd International Workshop on Open Services and Tools for Document Analysis. PDF icon Fast and Reliable Acquisition of Truth Data for Document Analysis using Cyclic Suggest Algorithms.pdf (776.45 KB)
Schall, M., Schambach M-P., & Franz M. O. (2016).  Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method. Conference: BW-CAR Symposium on Information and Communication Systems (SInCom). PDF icon 2016-12 Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method.pdf (803.54 KB)
Schall, M., Schambach M-P., & Franz M. O. (2016).  Increasing robustness of handwriting recognition using character n-gram decoding on large lexica. 12th IAPR International Workshop on Document Analysis Systems. PDF icon Schall et al_2016_Increasing robustness of handwriting recognition using character n-gram decoding on large lexica.pdf (440.82 KB)
Schall, M., Grunwald M., Umlauf G., & Franz M. O. (2015).  Radiometric calibration of digital cameras using Gaussian processes. SPIE Optics+ Optoelectronics. PDF icon Schall et al_2015_Radiometric calibration of digital cameras using Gaussian processes.PDF (953.2 KB)
Schall, M., Schambach M-P., & Franz M. O. (2019).  Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification. 15th IAPR International Conference on Document Analysis and Recognition. PDF icon Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification.pdf (553.24 KB)
Schall, M., Buehrig H. P., Schambach M-P., & Franz M. O. (2018).  LSTM Networks for Edit Distance Calculation with Exchangeable Dictionaries. 13th IAPR International Workshop on Document Analysis Systems. PDF icon 2018-04 LSTM Networks for Edit Distance Calculation with Exchangeable Dictionaries.pdf (145.78 KB)
Schall, M., Sacha D., Stein M., Franz M. O., & Keim D. A. (2018).  Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition. Visualization in Data Science (VDS at IEEE VIS) 2018. PDF icon Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition.pdf (1.03 MB)
Schall, M., Schambach M-P., & Franz M. O. (2018).  Multi-Dimensional Connectionist Classification: Reading Text in One Step. 13th IAPR International Workshop on Document Analysis Systems. PDF icon 2018-04 Multi-Dimensional Connectionist Classification Reading Text in One Step.pdf (485.57 KB)