Biblio
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(2022). Fast and memory-efficient independent component analysis using Lie group techniques.
International Conference on Curves and Surfaces.
(2022). DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency.
2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning. 
AHM2022_DeepDoubt.pdf (238.98 KB)
AHM2022_DeepDoubt.pdf (238.98 KB)
(2023). Deep transformation models for functional outcome prediction after acute ischemic stroke.
Biometrical Journal. 65, 2100379.
(2020). Integrating uncertainty in deep neural networks for MRI based stroke analysis.
Medical Image Analysis. 65, 101790.
(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.
(2023). Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model.
International Conference on Information Fusion (FUSION). 1-8.
(2021). Transformation models for flexible posteriors in variational bayes.
arXiv preprint. 2106.00528. 2106.00528.pdf (1.03 MB)
2106.00528.pdf (1.03 MB)
(1999). On robots and flies: Modeling the visual orientation behavior of flies.
Robotics and Autonomous Systems. 29, 227–242. Huber, Franz, Bülthoff_1999_On robots and flies Modeling the visual orientation behavior of flies.pdf (473.13 KB)
Huber, Franz, Bülthoff_1999_On robots and flies Modeling the visual orientation behavior of flies.pdf (473.13 KB)
(2003). Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.
(Nunes, U., de Almeida A., Bejczy A., Kosuge K., & Machado J., Ed.).{Proc. of the 11th International Conference on Advanced Robotics}. 2, 453–458. Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)
Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)
(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.
(2007). Nonlinear receptive field analysis: making kernel methods interpretable.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(2007). Center-surround filters emerge from optimizing predictivity in a free-viewing task.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(2009). Center-surround patterns emerge as optimal predictors for human saccade targets.
J. of Vision. 9, 1–15. Kienzle, Franz, Schölkopf_2009_Center-surround patterns emerge as optimal predictors for human saccade targets.pdf (900.5 KB)
Kienzle, Franz, Schölkopf_2009_Center-surround patterns emerge as optimal predictors for human saccade targets.pdf (900.5 KB)
(2005). Face detection – efficient and rank deficient.
(Saul, L. K., Weiss Y., & Bottou L., Ed.).{Advances in Neural Information Processing Systems 17}. 673–680. Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 KB)
Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 KB)
(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. Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
(2004). Efficient approximations for support vector machines for object detection.
(Rasmussen, C. E., Bülthoff H. H., & Giese M. A., Ed.).{Pattern Recognition, Proc. of the 26th DAGM Symposium}. 54–61. Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)
Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)
(2007). A nonparametric approach to bottom-up visual saliency.
(Schölkopf, B., Platt J., & Hoffmann T., Ed.).{Advances in Neural Information Processing Systems 19}. 19, 689–696. Kienzle et al._2007_A nonparametric approach to bottom-up visual saliency.pdf (879.52 KB)
Kienzle et al._2007_A nonparametric approach to bottom-up visual saliency.pdf (879.52 KB)
(2007). Learning the influence of spatio-temporal variations in local image structure on visual saliency.
(Bülthoff, H. H., Chatziastros A., Mallot H. A., & Ulrich R., Ed.).{Proc. 10. Tübinger Wahr\-neh\-mungs\-konferenz (TWK 2007)}. 63.
(2005). Learning an interest operator from eye movements.
{Proc. Workshop on Bioinspired Information Processing 2005}. Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
(2007). How to find interesting locations in video: a spatiotemporal interest point detector learned from human eye movements.
{Lecture Notes in Computer Science: Pattern Recognition (DAGM 2007)}. 405–417. Kienzle et al._2007_How to find interesting locations in video a spatiotemporal interest point detector learned from human eye movements.pdf (377.26 KB)
Kienzle et al._2007_How to find interesting locations in video a spatiotemporal interest point detector learned from human eye movements.pdf (377.26 KB)
(2006). Learning eye movements.
{Proc. Sensory Coding and the Natural Environment 2006}.
(2005). Iterative kernel principal component analysis for image modeling.
IEEE Trans. PAMI. 27, 1351 – 1366. Kim, Franz, Schölkopf_2005_Iterative Kernel Principal Component Analysis for Image Modeling.pdf (1.98 MB)
Kim, Franz, Schölkopf_2005_Iterative Kernel Principal Component Analysis for Image Modeling.pdf (1.98 MB)
(2004). Kernel Hebbian algorithm for single-frame super-resolution.
{Statistical Learning in Computer Vision (SLCV 2004), ECCV 2004 Workshop, Prague}. 135–149. Kim, Franz, Schölkopf_2004_Kernel Hebbian algorithm for single-frame super-resolution.pdf (2.22 MB)
Kim, Franz, Schölkopf_2004_Kernel Hebbian algorithm for single-frame super-resolution.pdf (2.22 MB)
(2022). Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2020). Deep and interpretable regression models for ordinal outcomes.
arXiv preprint. 2010.08376.