Biblio
Export 83 results:
Author [ Title] Type Year Filters: First Letter Of Last Name is H [Clear All Filters]
Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations.
2021 International Conference on Data Mining Workshops (ICDMW). 398-404.
(2021). Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations.
2021 International Conference on Data Mining Workshops (ICDMW). 398–404.
(2021). Adaptive and feature-preserving subdivision for high-quality tetrahedral meshes.
Computer Graphics Forum. 29, 117-127. AdaptiveSubTetraMeshes.pdf (1022.53 KB)
(2010). Adaptive tetrahedral subdivision for finite element analysis.
(.N., N., Ed.).Computer Graphics International, Singapore 2010. TetraSubFEA.pdf (3.43 MB)
(2010). Analyzing environmental conditions and vital signs to increase healthy living.
Mobile Networks for Biometric Data Analysis.
(2016). Automatic classification of non-small cell lung cancer histologic sub-types by deep learning.
VIRCHOWS ARCHIV. 108-108.
(2018). Automatic classification of non-small cell lung cancer histologic sub-types by deep learning.
VIRCHOWS ARCHIV. 108-108.
(2018). Biologically-inspired vs. CNN texture representations in novelty detection.
Applications of Machine Learning 2021. 118430I. Spie2021.pdf (5.33 MB)
(2021). Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms.
FIG Working Week 2020. Fig2020.pdf (876.57 KB)
(2020). Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms.
FIG Working Week 2020. Fig2020.pdf (876.57 KB)
(2020). Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms.
FIG Working Week 2020. Fig2020.pdf (876.57 KB)
(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). Deep and interpretable regression models for ordinal outcomes.
arXiv preprint. 2010.08376.
(2020). Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2022). Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2022). Deep transformation models for functional outcome prediction after acute ischemic stroke.
Biometrical Journal. 65, 2100379.
(2023). Deep transformation models for functional outcome prediction after acute ischemic stroke.
Biometrical Journal. 65, 2100379.
(2023). Deep transformation models for functional outcome prediction after acute ischemic stroke.
Biometrical Journal. 65, 2100379.
(2023). 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.
(2021). 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)
(2022). 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). 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).