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}.

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Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2020).  Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes. arXiv e-prints. 2010.08376.

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Prautzsch, H., & Umlauf G. (2006).  Parametrizations for triangular G^k spline surfaces of low degree. ACM Transactions on Graphics. 24, 1281-1293.

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GkSplineSurf.pdf (539.25 KB)

Bohnet, D., & Bonatti C. (2016).  Partially hyperbolic diffeomorphisms with uniformly compact center foliation: quotient dynamics. Ergodic Theory Dyn. Sys.. 36(4), 

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Dieterich, W., Dürr O., Pendzig P., Bunde A., & Nitzan A. (1999).  Percolation concepts in solid state ionics. Physica A: Statistical Mechanics and its Applications. 266, 229–237.

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Grunwald, M., Müller J., Schall M., Laube P., Umlauf G., & Franz M. O. (2015).  Pixel-wise Hybrid Image Registration on Wood Decors. BW-CAR| SINCOM. 24.

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Grunwald_2015_Pixel-wiseHybridImageRegistration.pdf (2.42 MB)

Yovel, Y., Franz M. O., Stilz P., & Schnitzler H.-U. (2008).  Plant classification from bat-like echolocation signals. PLoS Comput.\ Biol.. 4, e1000032.

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Yovel et al._2008_Plant classification from bat-like echolocation signals.pdf (772.54 KB)

Cieliebak, M., Dürr O., & Uzdilli F. (2013).  Potential and Limitations of Commercial Sentiment Detection Tools.. ESSEM@ AI* IA. 47–58.

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Dürr, O., Sick B., & Murina E. (2020).  Probabilistic deep learning: With python, keras and tensorflow probability.

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Dürr, O., Sick B., & Murina E. (2020).  Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability.

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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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Arpogaus2021_Probabilistic_Forecasting.pdf (427.35 KB)

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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Heyse, S., Brodte A., Bruttger O., Duerr O., Freeman T., Jung T., et al. (2005).  Quantifying bioactivity on a large scale: quality assurance and analysis of multiparametric ultra-HTS data. JALA: Journal of the Association for Laboratory Automation. 10, 207–212.

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Schall, M., Grunwald M., Umlauf G., & Franz M. O. (2015).  Radiometric calibration of digital cameras using Gaussian processes. SPIE Optics+ Optoelectronics.

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Schall et al_2015_Radiometric calibration of digital cameras using Gaussian processes.PDF (953.2 KB)

Grunwald, M., Laube P., Schall M., Umlauf G., & Franz M. O. (2017).  Radiometric calibration of digital cameras using neural networks. Optics and Photonics for Information Processing XI.

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Grunwald, M., Gansloser J., & Franz M. O. (2016).  Radiometric calibration of digital cameras using sparse Gaussian processes. Workshop Farbbildverarbeitung.

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Denker, K., Lehner B., & Umlauf G. (2011).  Real-time triangulation of point streams. Engineering with Computers. 27, 67-80.

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RTTriangulationPointStreams.pdf (1.05 MB)

Mallot, H. A., Gillner S., Steck S. D., & Franz M. O. (1999).  Recognition-triggered response and the view-graph approach to spatial cognition. (Freksa, C., & Mark D. M., Ed.).{Spatial Information Theory - Cognitive and Computational Foundations of Geographic Information Science (COSIT 99)}. 1661, 367-380.

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Ilg, W., Bakır G. H., Franz M. O., & Giese M. (2003).  A representation of complex movement sequences based on hierarchical spatio-temporal correspondence for imitation learning in robotics. (Bülthoff, H. H., Gegenfurtner K. R., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}. 74.

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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.

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Huber, S., Franz M. O., & Bülthoff H. H. (1999).  On robots and flies: Modeling the visual orientation behavior of flies. Robotics and Autonomous Systems. 29, 227–242.

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Huber, Franz, Bülthoff_1999_On robots and flies Modeling the visual orientation behavior of flies.pdf (473.13 KB)

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)}.

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Dürr, O., Duval F., Nichols A., Lang P., Brodte A., Heyse S., et al. (2007).  Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay. Journal of biomolecular screening. 12, 1042–1049.

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Franz, M. O., Kwon Y., Rasmussen C. E., & Schölkopf B. (2004).  Semi-supervised kernel regression using whitened function classes. (Rasmussen, C. E., Bülthoff H. H., Giese M. A., & Schölkopf B., Ed.).{Pattern Recognition, Proc.\ 26th DAGM Symposium}. 3175, 18 – 26.

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Franz et al._2004_Semi-supervised kernel regression using whitened function classes.pdf (198.7 KB)

Laube, P., & Umlauf G. (2016).  A short survey on recent methods for cage computation. BW-CAR| SINCOM. 37.

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cagesurvSinCom.pdf (444.89 KB)