Biblio

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I
Barbero, A., Franz M. O., van Drongelen W., Dorronsoro J. R., Schölkopf B., & Grosse-Wentrup M. (2009).  Implicit Wiener series analysis of epileptic seizure recordings.. {Ann. Intl. Conf. of the IEEE Engineering in Medicine and Biology Society}. 5304–5307.PDF icon Barbero et al._2009_Implicit Wiener series analysis of epileptic seizure recordings.pdf (327.26 KB)
Franz, M. O., & Schölkopf B. (2004).  Implicit Wiener series for capturing higher-order interactions in images. (Olshausen, B. A., & Lewicki M., Ed.).{Proc. Sensory Coding and the Natural Environment 2004}.
Franz, M. O., Macke J. H., Saleem A., & Schultz S. R. (2007).  Implicit Wiener series for estimating nonlinear receptive fields. {Proc. 31st Göttingen Neurobiolgy Conf.}. 1199.
Prautzsch, H., & Umlauf G. (1999).  Improved triangular subdivision schemes. (Wolter, F.-E., & Patrikalakis N.M., Ed.).Proceedings of the CGI '98. PDF icon TriSub.pdf (1.73 MB)
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)
Franzini, A., Dürr O., Baty F., Hosang J., & Brutsche M. H. (2014).  In Silico Identification of Cell-type-specific Compartmental Gene Expression Signatures with Predictive Value for Response to Erlotinib/bevacizumab Therapy in Non-small Cell Lung Cancer (nsclc). Respiration. 87, 562.
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)
Hermann, M., Umlauf G., Goldlücke B., & Franz M. O. (2023).  Incremental one-class learning using regularized null-space training for industrial defect detection. 16th International Conference on Machine Vision (ICMV).
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)
Franz, M. O., Chahl J. S., & Krapp H. G. (2004).  Insect-inspired estimation of egomotion.. Neural Computation. 16, 2245–60.
Franz, M. O., & Chahl J. S. (2002).  Insect-inspired estimation of self-motion. (Bülthoff, H. H., Lee S.-W., Poggio T. A., & Wallraven C., Ed.).{Proc. 2nd Workshop on Biologically Motivated Computer Vision (BMCV 2002)}. 2525, 171-180.PDF icon Franz, Chahl_2002_Insect-inspired estimation of self-motion.pdf (274.5 KB)
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.
Sick, B., & Dürr O. (2025).  Interpretable Neural Causal Models with TRAM-DAGs. (Huang, B., & Drton M., Ed.).Proceedings of the Fourth Conference on Causal Learning and Reasoning. 606–630.
Burkhart, D., Hamann B., & Umlauf G. (2010).  Iso-geometric analysis based on Catmull-Clark solid subdivision. Computer Graphics Forum. 29, 1575-1784.PDF icon IsoCatmullClarkSub.pdf (3.69 MB)
Bobach, T., Bertram M., & Umlauf G. (2006).  Issues and implementation of C^1 and C^2 natural neighbor interpolation. (G. al., B. et, Ed.).Advances in Visual Computing. Part II. PDF icon C1C2NeighborInterp.pdf (3.87 MB)
Kim, K. I., Franz M. O., & Schölkopf B. (2005).  Iterative kernel principal component analysis for image modeling. IEEE Trans. PAMI. 27, 1351 – 1366.PDF icon Kim, Franz, Schölkopf_2005_Iterative Kernel Principal Component Analysis for Image Modeling.pdf (1.98 MB)
J
Dürr, O., Uzdilli F., & Cieliebak M. (2014).  JOINT\_FORCES: Unite Competing Sentiment Classifiers with Random Forest.. SemEval@ COLING. 366–369.
K
Kim, K. I., Franz M. O., & Schölkopf B. (2004).  Kernel Hebbian algorithm for single-frame super-resolution. {Statistical Learning in Computer Vision (SLCV 2004), ECCV 2004 Workshop, Prague}. 135–149.PDF icon Kim, Franz, Schölkopf_2004_Kernel Hebbian algorithm for single-frame super-resolution.pdf (2.22 MB)
Dürr, O., Murina E., Siegismund D., Tolkachev V., Steigele S., & Sick B. (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)
L
Hermann, M., Umlauf G., & Franz M. O. (2022).  Large-scale independent component analysis by speeding up Lie group techniques. International Conference on Acoustics, Speech, and Signal Processing, ICASSP. PDF icon conference_101719.pdf (646.58 KB)
Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2005).  Learning an interest operator from eye movements. {Proc. Workshop on Bioinspired Information Processing 2005}. PDF icon Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2006).  Learning an interest operator from human eye movements. (Schmid, C., Soatto S., & Tomasi C., Ed.).{Beyond Patches Workshop, Intl. Conf. on Computer Vision and Pattern Recognition}. 1–8.PDF icon Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
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)
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.
Lukic, Y. X., Vogt C., Dürr O., & Stadelmann T. (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)

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