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<!--HTML-->Starting from a brief historical perspective on scientific discovery, this talk will review some of the theory and open problems of deep learning and describe how to design efficient feedforward and recursive deep learning architectures for applications in the natural sciences. In...

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Autor principal: KÉGL, Balázs
Lenguaje:eng
Publicado: 2015
Materias:
Acceso en línea:http://cds.cern.ch/record/2093597
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author KÉGL, Balázs
author_facet KÉGL, Balázs
author_sort KÉGL, Balázs
collection CERN
description <!--HTML-->Starting from a brief historical perspective on scientific discovery, this talk will review some of the theory and open problems of deep learning and describe how to design efficient feedforward and recursive deep learning architectures for applications in the natural sciences. In particular, the focus will be on multiple particle problems at different scales: in biology (e.g. prediction of protein structures), chemistry (e.g. prediction of molecular properties and reactions), and high-energy physics (e.g. detection of exotic particles, jet substructure and tagging, "dark matter and dark knowledge")
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2015
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spelling cern-20935972022-11-02T22:33:39Zhttp://cds.cern.ch/record/2093597engKÉGL, BalázsResponseData Science @ LHC 2015 WorkshopLPCC Workshops<!--HTML-->Starting from a brief historical perspective on scientific discovery, this talk will review some of the theory and open problems of deep learning and describe how to design efficient feedforward and recursive deep learning architectures for applications in the natural sciences. In particular, the focus will be on multiple particle problems at different scales: in biology (e.g. prediction of protein structures), chemistry (e.g. prediction of molecular properties and reactions), and high-energy physics (e.g. detection of exotic particles, jet substructure and tagging, "dark matter and dark knowledge")oai:cds.cern.ch:20935972015
spellingShingle LPCC Workshops
KÉGL, Balázs
Response
title Response
title_full Response
title_fullStr Response
title_full_unstemmed Response
title_short Response
title_sort response
topic LPCC Workshops
url http://cds.cern.ch/record/2093597
work_keys_str_mv AT keglbalazs response
AT keglbalazs datasciencelhc2015workshop