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dc.contributor.author
Ganea, Octavian-Eugen
dc.contributor.author
Bécigneul, Gary
dc.date.accessioned
2020-01-30T12:12:33Z
dc.date.available
2020-01-24T17:26:19Z
dc.date.available
2020-01-30T12:12:33Z
dc.date.issued
2018-10
dc.identifier.uri
http://hdl.handle.net/20.500.11850/393955
dc.language.iso
en
en_US
dc.title
Riemannian Adaptive Optimization Methods
en_US
dc.type
Conference Paper
ethz.size
16 p.
en_US
ethz.event
7th International Conference on Learning Representations (ICLR 2019)
en_US
ethz.event.location
New Orleans, LA, USA
en_US
ethz.event.date
May 6-9, 2019
en_US
ethz.notes
Conference lecture held on May 8, 2019
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09462 - Hofmann, Thomas / Hofmann, Thomas
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09462 - Hofmann, Thomas / Hofmann, Thomas
en_US
ethz.date.deposited
2020-01-24T17:26:30Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-01-30T12:12:43Z
ethz.rosetta.lastUpdated
2020-01-30T12:12:43Z
ethz.rosetta.versionExported
true
ethz.COinS
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