Biblio
]
(2024). Bayesian Semi-structured Subspace Inference.
(Dasgupta, S., Mandt S., & Li Y., Ed.).Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. 1819–1827.
(2021). Biologically-inspired vs. CNN texture representations in novelty detection.
Applications of Machine Learning 2021. 118430I.
Spie2021.pdf (5.33 MB)
Spie2021.pdf (5.33 MB)
(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. Franz et al._1999_Can fly tangential neurons be used to estimate self-motion.pdf (170.74 KB)
Franz et al._1999_Can fly tangential neurons be used to estimate self-motion.pdf (170.74 KB)
(2018). Capturing Suprasegmental Features of a Voice with RNNs for Improved Speaker Clustering.
IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 333–345. ANNPR_2018b.pdf (692.47 KB)
ANNPR_2018b.pdf (692.47 KB)
(2007). Center-surround filters emerge from optimizing predictivity in a free-viewing task.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(2013). A compact high-performance frequency reference for space applications.
{29th Intl. Symposium on Space Technology and Science (ISTS 2013), Nagoya (Japan)}. Schuldt et al._2013_A Compact High-Performance Frequency Reference for Space Applications.pdf (369.85 KB)
Schuldt et al._2013_A Compact High-Performance Frequency Reference for Space Applications.pdf (369.85 KB)
(2006). Comparison of Voronoi based scatterd data interpolation schemes.
(Villanueva, J.J., Ed.).Proceedings of the Internationl Conference on Visualization, Imaging and Image Processing. VoronoiInterp.pdf (4.63 MB)
VoronoiInterp.pdf (4.63 MB)
(2003). Constraints measures and reproduction of style in robot imitation learning.
(Bülthoff, H. H., Gegenfurtner K. R., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}. 70.
(2006). Controlling a subdivision tuning method.
(Cohen, A., Merrien J.-L., & Schumaker L.L., Ed.).Curve and Surface Fitting. SubTuning.pdf (553.79 KB)
SubTuning.pdf (553.79 KB)
(2015). Deep Learning on a Raspberry Pi for Real Time Face Recognition..
Eurographics (Posters). 11–12.
(2018). Deep Learning Parametrization for B-Spline Curve Approximation.
2018 International Conference on 3D Vision (3DV). 691–699. 0109.pdf (675.91 KB)
0109.pdf (675.91 KB)
(2026). A Deep Network for Object Detection on Inland Waters.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 6590-6599.
(2021). Deep transformation models: Tackling complex regression problems with neural network based transformation models.
Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021.
(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)
(2017). Detection of variations in holter ECG recordings based on dynamic cluster analysis.
International Conference on Intelligent Decision Technologies.
(2023). Development and application of a climate emulator.
EGU23.
(2007). Discrete harmonic functions from local coordinates.
(Martin, R., Sabin M., & Winkler J., Ed.).Mathematics of Surfaces XII. HarmonicFunc.pdf (835.96 KB)
HarmonicFunc.pdf (835.96 KB)
(2019). Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification.
15th IAPR International Conference on Document Analysis and Recognition. Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification.pdf (553.24 KB)
Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification.pdf (553.24 KB)
(2025). Dynamic Use-Phase Variability In IoT-Enabled Systems: A Simulation-Based Case Study Of Smart Thermostats.
ECMS 2025 Proceedings edited by Marco Scarpa, Salvatore Cavalieri, Salvatore Serrano, Fabrizio De Vita. 711–717.
(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). Embedded vertex shader in FPGA.
(A. al., R. et, Ed.).Embedded System Design: Topics, Techniques and Trends.
(2024). Enhancing Inland Water Safety: The Lake Constance Obstacle Detection Benchmark.
IEEE International Conference on Robotics and Automation (ICRA). 14808-14814.
(2017). Evaluation of features for SVM-based classification of geometric primitives in point clouds.
Machine Vision Applications (MVA), 2017 Fifteenth IAPR International Conference on. 59–62. paper.pdf (1.5 MB)
paper.pdf (1.5 MB)
(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.
(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)