Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Fabian Küppers"'
Publikováno v:
Magnetic Resonance in Medicine. 88:1608-1623
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250712
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63cedf9602d8e474dc7e6fe7905bb2be
https://doi.org/10.1007/978-3-031-25072-9_30
https://doi.org/10.1007/978-3-031-25072-9_30
Publikováno v:
Magnetic resonance in medicineREFERENCES. 88(4)
The simultaneous quantification of TThis work presents a novel 10-echo GE-SE EPIK (EPI with keyhole) sequence for the rapid quantification of TIn comparison with repeated single-echo SE, multi-echo GE, and spectroscopy methods, the GE-SE EPIK sequenc
Autor:
Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi-Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle
Publikováno v:
Deep Neural Networks and Data for Automated Driving ISBN: 9783031012327
Fingscheidt, Gottschalk et al. (Hg.): Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety
Fingscheidt, Gottschalk et al. (Hg.): Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a97320425a5b7fac20b0272cdc9fe19b
https://doi.org/10.1007/978-3-031-01233-4_1
https://doi.org/10.1007/978-3-031-01233-4_1
Publikováno v:
CVPR Workshops
In this paper, we propose a method for post-hoc ex-plainability of black-box models. The key component of the semantic and quantitative local explanation is a knowledge distillation (KD) process which is used to mimic the teacher’s behavior by mean
Modern neural networks have found to be miscalibrated in terms of confidence calibration, i.e., their predicted confidence scores do not reflect the observed accuracy or precision. Recent work has introduced methods for post-hoc confidence calibratio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9aa998d6f5bed685c9e0059056751121
Publikováno v:
CVPR Workshops
Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of
Publikováno v:
EUSIPCO
Heavy machine tools are used in numerous industrial manufacturing processes. Avoiding unplanned maintenance time is crucial and can be achieved by continuous condition monitoring. A more targeted condition monitoring is possible if the operating stat
Publikováno v:
Volume 2D: Turbomachinery.
This paper presents a first step to adapt deep neural networks (DNN) to turbomachinery designs. It is demonstrated that DNNs can predict complete flow solutions, using xyzcoordinates of the CFD mesh, rotational speed and boundary conditions as input