Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Michael Mock"'
Autor:
Lesley Osborn, David Meyer, Paul Dahm, Brandy Ferguson, Rodolfo Cabrera, Damon Sanger, Michael Mock, Tony Herrera, Shelby Mader, Luis Ostrosky‐Zeichner
Publikováno v:
Journal of the American College of Emergency Physicians Open, Vol 1, Iss 4, Pp 557-562 (2020)
Abstract There is limited guidance on the use of helicopter medical personnel to facilitate care of critically ill COVID‐19 patients. This manuscript describes the emergence of this novel virus, its mode of transmission, and the potential impacts o
Externí odkaz:
https://doaj.org/article/eac9984a03a54a1b98de2ec72ecfb8aa
Autor:
Mohamed Ashmwe, Katja Posa, Alexander Rührnößl, Johannes Christoph Heinzel, Patrick Heimel, Michael Mock, Barbara Schädl, Claudia Keibl, Sebastien Couillard-Despres, Heinz Redl, Rainer Mittermayr, David Hercher
Publikováno v:
Biomedicines, Vol 10, Iss 7, p 1630 (2022)
Extracorporeal shockwave therapy (ESWT) can stimulate processes to promote regeneration, including cell proliferation and modulation of inflammation. Specific miRNA expression panels have been established to define correlations with regulatory target
Externí odkaz:
https://doaj.org/article/56f9392e3da3444fbb91ef1055a8d1f1
The application of AI is a key enabler for highly automated driving. Initiated by VDA, a consortium of OEMs, suppliers, technology providers and scientific institutions is developing a methodology for a novel safety argumentation in the project “KI
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4472ad0f1bbc5a8cc994fd703e71f9f8
https://publica.fraunhofer.de/handle/publica/419947
https://publica.fraunhofer.de/handle/publica/419947
Publikováno v:
Deep Neural Networks and Data for Automated Driving ISBN: 9783031012327
The latest generation of safety standards applicable to automated driving systems require both qualitative and quantitative safety acceptance criteria to be defined and argued. At the same time, the use of machine learning (ML) functions is increasin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::04b1f3886196a67e0928eefb1d0a44fd
https://doi.org/10.1007/978-3-031-01233-4_12
https://doi.org/10.1007/978-3-031-01233-4_12
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:
Regulierung für Algorithmen und Künstliche Intelligenz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ef0a7f04728802a97357b23a68c1da54
https://doi.org/10.5771/9783748927990-175
https://doi.org/10.5771/9783748927990-175
Autor:
Fabian Hüger, Michael Mock, Stephan Dr. Scholz, Loren Schwarz, Thomas Stauner, Andreas J. Rohatschek, Frederik Blank
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030839055
SAFECOMP Workshops
SAFECOMP Workshops
Developing a stringent safety argumentation for AI-based perception functions requires a complete methodology to systematically organize the complex interplay between specifications, data and training of AI-functions, safety measures and metrics, ris
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a23508feef4a21c2462c82f4a8f588ec
https://doi.org/10.1007/978-3-030-83906-2_21
https://doi.org/10.1007/978-3-030-83906-2_21
Publikováno v:
CVPR Workshops
An important pillar for safe machine learning (ML) is the systematic mitigation of weaknesses in neural networks to afford their deployment in critical applications. An ubiquitous class of safety risks are learned shortcuts, i.e. spurious correlation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a57ec151c7c28bbcd368412ea0473d2
Autor:
Shelby Mader, David E. Meyer, Damon Sanger, Brandy Ferguson, Paul Dahm, Tony Herrera, Luis Ostrosky-Zeichner, Lesley Osborn, Michael Mock, Rodolfo Cabrera
Publikováno v:
Journal of the American College of Emergency Physicians Open
Journal of the American College of Emergency Physicians Open, Vol 1, Iss 4, Pp 557-562 (2020)
Journal of the American College of Emergency Physicians Open, Vol 1, Iss 4, Pp 557-562 (2020)
There is limited guidance on the use of helicopter medical personnel to facilitate care of critically ill COVID‐19 patients. This manuscript describes the emergence of this novel virus, its mode of transmission, and the potential impacts on patient
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030438869
PKDD/ECML Workshops (2)
PKDD/ECML Workshops (2)
The performance of modern relation extraction systems is to a great degree dependent on the size and quality of the underlying training corpus and in particular on the labels. Since generating these labels by human annotators is expensive, Distant Su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::90bcb3b9cdeb7b20831601403ec96915
https://doi.org/10.1007/978-3-030-43887-6_6
https://doi.org/10.1007/978-3-030-43887-6_6