Biblio
Export 36 results:
Author Title Type [ Year] Filters: First Letter Of Last Name is K [Clear All Filters]
3D-Extended Object Tracking and Shape Classification with a Lidar Sensor using Random Matrices and Virtual Measurement Models.
27th International Conference on Information Fusion (FUSION). 1-8.
(2024). Deep transformation models for functional outcome prediction after acute ischemic stroke.
Biometrical Journal. 65, 2100379.
(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.
(2023). Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2022). Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms.
FIG Working Week 2020. Fig2020.pdf (876.57 KB)
(2020). Deep and interpretable regression models for ordinal outcomes.
arXiv preprint. 2010.08376.
(2020). Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes.
arXiv e-prints. 2010.08376.
(2020). Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition.
Visualization in Data Science (VDS at IEEE VIS) 2018. Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition.pdf (1.03 MB)
(2018). Analyzing environmental conditions and vital signs to increase healthy living.
Mobile Networks for Biometric Data Analysis.
(2016). Design of a dual species atom interferometer for space.
Experimental Astronomy. 39, 167-206. Schuldt et al._2015_Design of a dual species atom interferometer for space.pdf (2.98 MB)
(2015). Design of a dual species atom interferometer for space.
Experimental Astronomy. 39, 167-206. Schuldt et al._2015_Design of a dual species atom interferometer for space.pdf (2.98 MB)
(2015). Gene expression signatures predictive of bevacizumab/erlotinib therapeutic benefit in advanced non-squamous non-small cell lung cancer patients (SAKK 19/05 trial).
Clinical Cancer Research. clincanres––3135.
(2015). STE-QUEST — Test of the universality of free fall using cold atom interferometry.
Classical and Quantum Gravity. 31, 115010. Aguilera, Ahlers_2014_STE-QUEST—test of the universality of free fall using cold atom interferometry.pdf (778.56 KB)
(2014). STE-QUEST — Test of the universality of free fall using cold atom interferometry.
Classical and Quantum Gravity. 31, 115010. Aguilera, Ahlers_2014_STE-QUEST—test of the universality of free fall using cold atom interferometry.pdf (778.56 KB)
(2014). 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)
(2013).
(2009).
Center-surround filters emerge from optimizing predictivity in a free-viewing task.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(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)
(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.
(2007). Nonlinear receptive field analysis: making kernel methods interpretable.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(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)
(2007). 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)
(2006). Learning eye movements.
{Proc. Sensory Coding and the Natural Environment 2006}.
(2006). Natural neighbor interpolation and order of continuity.
(Hagen, H., Kerren A., & Dannenmann P., Ed.).GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets. NatNeighborInterp.pdf (1.47 MB)
(2006). Topographic distance functions for interpolation of meteorological data.
(Hagen, H., Kerren A., & Dannenmann P., Ed.).GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets. TopoDistFunc.pdf (2.27 MB)
(2006).