The Nature of SGD Optimization
From Implicit Regularization to Generalization in Deep Learning
dc.contributor.author
Pinto, Andrea
dc.contributor.supervisor
Poggio, Tomaso
dc.contributor.supervisor
Hofmann, Thomas
dc.date.accessioned
2024-06-25T09:48:47Z
dc.date.available
2024-06-25T08:41:52Z
dc.date.available
2024-06-25T09:48:47Z
dc.date.issued
2024
dc.identifier.uri
http://hdl.handle.net/20.500.11850/679918
dc.identifier.doi
10.3929/ethz-b-000679918
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.title
The Nature of SGD Optimization
en_US
dc.type
Master Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.title.subtitle
From Implicit Regularization to Generalization in Deep Learning
en_US
ethz.size
100 p.
en_US
ethz.publication.place
Zurich
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.date.deposited
2024-06-25T08:41:52Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2024-06-25T09:48:48Z
ethz.rosetta.lastUpdated
2024-06-25T09:48:48Z
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true
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Master Thesis [2047]