Biblio
Export 41 results:
Author Title Type [ Year
] Filters: First Letter Of Last Name is K [Clear All Filters]
(2025). Paths and Ambient Spaces in Neural Loss Landscapes.
(Li, Y., Mandt S., Agrawal S., & Khan E., Ed.).Proceedings of The 28th International Conference on Artificial Intelligence and Statistics. 10–18.
(2025). Paths and Ambient Spaces in Neural Loss Landscapes.
(Li, Y., Mandt S., Agrawal S., & Khan E., Ed.).Proceedings of The 28th International Conference on Artificial Intelligence and Statistics. 10–18.
(2024). 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). Bernstein flows for flexible posteriors in variational Bayes.
AStA Advances in Statistical Analysis. 108, 375–394.
(2024). Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo.
Journal of Statistical Software. 111,
(2023). 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.
(2022). Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2022). Design and Calibration of Plane Mirror Setups for Mobile Robots with a 2D-Lidar.
Sensors. 22, 7830.
(2020). 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.
(2018). 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)
(2016). Analyzing environmental conditions and vital signs to increase healthy living.
Mobile Networks for Biometric Data Analysis.
(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). 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.
(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). 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)
(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)
(2007). 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)}.
