Biblio

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Dürr, O., Sick B., & Murina E. (2020).  Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability.
Dürr, O., Sick B., & Murina E. (2020).  Probabilistic deep learning: With python, keras and tensorflow probability.
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.
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. PDF icon Arpogaus2021_Probabilistic_Forecasting.pdf (427.35 KB)
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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. PDF icon 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.
Grunwald, M., Gansloser J., & Franz M. O. (2016).  Radiometric calibration of digital cameras using sparse Gaussian processes. Workshop Farbbildverarbeitung.
Denker, K., Lehner B., & Umlauf G. (2011).  Real-time triangulation of point streams. Engineering with Computers. 27, 67-80.PDF icon 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.
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.
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.
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.PDF icon 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)}.
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.PDF icon 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.PDF icon cagesurvSinCom.pdf (444.89 KB)
Arpogaus, M., Voss M., Sick B., Nigge-Uricher M., & Dürr O. (2023).  Short-term density forecasting of low-voltage load using Bernstein-polynomial normalizing flows. IEEE Transactions on Smart Grid.
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)
Le, P. H. D., & Franz M. O. (2010).  Single band statistics and steganalysis performance. {Proc. 6th Intl. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP-2010)}. 188–191.PDF icon Le, Franz_2010_Single Band Statistics and Steganalysis Performance.pdf (206.46 KB)
Brach, K., Sick B., & Dürr O. (2020).  Single Shot MC Dropout Approximation. ICML Workshop on Uncertainty and Robustness in Deep Learning.
Dürr, O., & Sick B. (2016).  Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks. Journal of biomolecular screening. 21, 998–1003.
Brach, K., Sick B., & Dürr O. (2023).  Single-shot Bayesian approximation for neural networks.
Homburger, H., Wirtensohn S., Hoher P., Baur T., Griesser D., Diehl M., et al. (2025).  Solgenia—A test vessel toward energy-efficient autonomous water taxi applications. Ocean Engineering.
Lukic, Y., Vogt C., Dürr O., & Stadelmann T. (2016).  Speaker Identification and Clustering using Convolution Neural Networks. IEEE International workshop on Machine Learning for Signal Processing.

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