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Algorithms, Applications and Theoret...

Shaham, Uri.

Algorithms, Applications and Theoretical Properties of Deep Neural Networks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Algorithms, Applications and Theoretical Properties of Deep Neural Networks./
Author:	Shaham, Uri.
Description:	1 online resource (156 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355028126

Algorithms, Applications and Theoretical Properties of Deep Neural Networks.
Shaham, Uri.

Algorithms, Applications and Theoretical Properties of Deep Neural Networks. - 1 online resource (156 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Various theoretical and practical aspects of deep learning are discussed. We present approximation bounds for deep neural networks and their dependence on the data manifold and target function complexity. We propose a robust optimization procedure for neural networks and non-parametric models, and present applications of deep learning to unsupervised ensemble learning, common variable learning and batch effect removal.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355028126Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Algorithms, Applications and Theoretical Properties of Deep Neural Networks.
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020 $a 9780355028126
035 $a (MiAaPQ)AAI10632563
035 $a AAI10632563
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Shaham, Uri. $3 1180923
245 1 0 $a Algorithms, Applications and Theoretical Properties of Deep Neural Networks.
264 0 $c 2017
300 $a 1 online resource (156 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Advisers: Ronald R. Coifman; Sahand Negahban.
502 $a Thesis (Ph.D.) $c Yale University $d 2017.
504 $a Includes bibliographical references
520 $a Various theoretical and practical aspects of deep learning are discussed. We present approximation bounds for deep neural networks and their dependence on the data manifold and target function complexity. We propose a robust optimization procedure for neural networks and non-parametric models, and present applications of deep learning to unsupervised ensemble learning, common variable learning and batch effect removal.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Mathematics. $3 527692
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
690 $a 0405
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Yale University. $3 1178968
773 0 $t Dissertation Abstracts International $g 78-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10632563 $z click for full text (PQDT)

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