Biblio

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D
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)
Dürr, O., Dieterich W., Maas P., & Nitzan A. (2002).  Effective medium theory of conduction in stretched polymer electrolytes. arXiv preprint cond-mat/0202165.
Dürr, O., Sick B., & Murina E. (2020).  Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability.
Dürr, O., Volz T., Dieterich W., & Nitzan A. (2002).  Dynamic percolation theory for particle diffusion in a polymer network. The Journal of chemical physics. 117, 441–447.
Dürr, O., Hörtling S., Dold D., Kovylov I., & Sick B. (2024).  Bernstein flows for flexible posteriors in variational Bayes. AStA Advances in Statistical Analysis. 108, 375–394.
Dürr, O., Pauchard Y., Browarnik D., Axthelm R., & Loeser M. (2015).  Deep Learning on a Raspberry Pi for Real Time Face Recognition.. Eurographics (Posters). 11–12.
Dürr, O., & Dieterich W. (2007).  Glassy and Polymeric Ionic Conductors: Statistical Modeling and Monte Carlo Simulations. Superionic Conductor Physics. 1, 77–80.
F
Franz, M. O., Schölkopf B., Mallot H. A., Bülthoff H. H., & Zell A. (1998).  Navigation mit Schnappschüssen.. (Levi, P., Ahlers R.-J., May F., & Schanz M., Ed.).{Mustererkennung 1998. Proc. of the 20th DAGM-Symposium}. 412-428.PDF icon Franz et al._1998_Navigation mit Schnappschüssen.pdf (300.5 KB)
Franz, M. O., & Krapp H. G. (2000).  Wide-field, motion-sensitive neurons and matched filters for optic flow fields. Biol. Cybern.. 83, 185 – 197.PDF icon Franz, Krapp_2000_Wide-field, motion-sensitive neurons and matched filters for optic flow fields.pdf (261.7 KB)
Franz, M. O., Schölkopf B., Mallot H. A., & Bülthoff H. H. (1998).  Learning view graphs for robot navigation. Autonomous Robots. 5, 111 – 125.PDF icon Franz et al._1998_Learning View Graphs for Robot Navigation.pdf (1.26 MB)
Franz, M. O., Stürzl W., Hübner W., & Mallot H. A. (2008).  A robot system for biomimetic navigation - from snapshots to metric embeddings of view graphs. (Yeap, A. W., & Jefferies M., Ed.).{Robotics and Cognitive Approaches to Spatial Mapping}. 38, 297–314.
Franz, M. O. (2002).  Optimal linear estimation of self-motion - a real-world test of a model of fly tangential neurons. (Prescott, T., & Webb B., Ed.).{SAB02 Workshop on Robotics as Theoretical Biology}.
Franz, M. O. (2003).  Robots with cognition?. (Bülthoff, H. H., Gegenfurtner K. R., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}.
Franz, M. O., & Schölkopf B. (2006).  A unifying view of Wiener and Volterra theory and polynomial kernel regression. Neural Computation. 18, 3097 – 3118.PDF icon Franz, Schölkopf_2006_A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression.pdf (165.97 KB)
Franz, M. O., & Chahl J. S. (2003).  Linear combinations of optic flow vectors for estimating self-motion-a real-world test of a neural model. (Becker, S., Obermayer K., & Thrun S., Ed.).{Advances in Neural Information Processing Systems 15}. 1343–1350.
Franz, M. O., Hengstenberg R., & Krapp H. G. (1998).  VS-neurons as matched filters for self-motion-induced optic flow fields. (Elsner, N., & Wehner R., Ed.).{New Neuroethology on the Move}. II, 419.
Franz, M. O., & Zhang M. (1995).  Supression and creation of chaos in a periodically forced Lorenz system. Phys. Rev. E. 52, 3558–3565.PDF icon Franz, Zhang_1995_Supression and creation of chaos in a periodically forced Lorenz system.pdf (1.45 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)
Franz, M. O., & Schölkopf B. (2006).  Implicit Volterra and Wiener series for higher-order image analysis. {Advances in Data Analysis 30th Ann. Conf. German Classification Society}. 60.
Franz, M. O., Neumann T. R., Plagge M., Mallot H. A., & Zell A. (1999).  Can fly tangential neurons be used to estimate self-motion?. (Willshaw, D., & Murray A., Ed.).{Proc. of the 9th Intl. Conf. on Artificial Neural Networks (ICANN 1999)}. CP 470, 994-999.PDF icon Franz et al._1999_Can fly tangential neurons be used to estimate self-motion.pdf (170.74 KB)
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)
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., Schölkopf B., Georg P., Mallot H. A., & Bülthoff H. H. (1997).  Learning view graphs for robot navigation. (Johnson, W. L., Ed.).{Proc.\ 1.İntl.\ Conf.\ on Autonomous Agents}. 138 – 147.PDF icon Franz et al._1998_Learning View Graphs for Robot Navigation.pdf (1.26 MB)
Franz, M. O., Chahl J. S., & Krapp H. G. (2004).  Insect-inspired estimation of egomotion.. Neural Computation. 16, 2245–60.
Franz, M. O., & Mallot H. A. (2000).  Biomimetic robot navigation. Robotics and Autonomous Systems. 30, 133 – 153.PDF icon Franz, Mallot_2000_Biomimetic robot navigation.pdf (171.77 KB)

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