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Informed Machine Learning for Industry

 
: Bauckhage, Christian; Schulz, Daniel; Hecker, Dirk

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ERCIM News (2019), Nr.116, S.37-38
ISSN: 0926-4981
ISSN: 1564-0094
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
Deep neural networks have pushed the boundaries of artificial intelligence but their training requires vast amounts of data and high performance hardware. While truly digitised companies easily cope with these prerequisites, traditional industries still often lack the kind of data or infrastructures the current generation of end-to-end machine learning depends on. The Fraunhofer Center for Machine Learning therefore develops novel solutions which are informed by expert knowledge. These typically require less training data and are more transparent in their decision-making processes.

: http://publica.fraunhofer.de/dokumente/N-593202.html