Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Bockermann, Christian"'
Once completed, the Cherenkov Telescope Array (CTA) will be able to map the gamma-ray sky in a wide energy range from several tens of GeV to some hundreds of TeV and will be more sensitive than previous experiments by an order of magnitude. It opens
Externí odkaz:
http://arxiv.org/abs/1809.00581
Publikováno v:
In Information Systems March 2017 64:258-265
Autor:
Bunse, Mirko, Bockermann, Christian, Buss, Jens, Morik, Katharina, Rhode, Wolfgang, Ruhe, Tim
Publikováno v:
ASP Conference Series; 2019, Vol. 522, p417-420, 4p
Publikováno v:
ASP Conference Series; 2019, Vol. 522, p319-322, 4p
Autor:
Buss, Jens, Bockermann, Christian, Adam, Jan, Ahnen, Max, Baack, Dominik, Balbo, Matteo, Bergmann, Matthias, Biland, Adrian, Blank, Michael, Bretz, Thomas, Bruegge, Kai, Dmytriiev, Anton, Domer, Daniela, Egorov, Alexey, Einecke, Sabrina, Hempfling, Christina, Hildebrand, Dorothee, Hughes, Gareth, Linhoff, Lena, Mannheim, Karl
Publikováno v:
ASP Conference Series; 2019, Vol. 521, p584-587, 4p
Autor:
Bruegge, Kai, Adam, Jan, Ahnen, Max, Baack, Dominik, Balbo, Matteo, Bergmann, Matthias, Biland, Adrian, Bockermann, Christian, Buss, Jens, Blank, Michael, Bretz, Thomas, Dmytriiev, Anton, Dorner, Daniela, Egorov, Alexey, Einecke, Sabrina, Hempfling, Christina, Hildebrand, Dorothee, Hughes, Gareth, Linhoff, Lena, Mannheim, Karl
Publikováno v:
ASP Conference Series; 2019, Vol. 521, p335-338, 4p
Autor:
Bockermann, Christian
The continuous processing of streaming data has become an important aspect in many applications. Over the last years a variety of different streaming platforms has been developed and a number of open source frameworks is available for the implementat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db4a1dbca314f5e2ad13bb10b66bf8e6
http://hdl.handle.net/2003/37175
http://hdl.handle.net/2003/37175
Publikováno v:
KI: Künstliche Intelligenz; Feb2018, Vol. 32 Issue 1, p27-36, 10p
Autor:
Bockermann, Christian, Lee, Sangkyun
Stochastic gradient descent methods have been quite successful for solving large- scale and online learning problems. We provide a simple parallel framework to obtain solutions of high confidence, where the confidence can be easily controlled by the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4831e5b6fbc04a575e46b2f1eadbbd21
http://hdl.handle.net/2003/29345
http://hdl.handle.net/2003/29345