Biblio
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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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
(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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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)
(2005). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)