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Inference on Graphs : = From Probabi...

University of California, Berkeley.

Inference on Graphs : = From Probability Methods to Deep Neural Networks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Inference on Graphs :/
Reminder of title:	From Probability Methods to Deep Neural Networks.
Author:	Li, Xiang.
Description:	1 online resource (72 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:	9780355034097

Inference on Graphs : = From Probability Methods to Deep Neural Networks.
Li, Xiang.

Inference on Graphs :From Probability Methods to Deep Neural Networks. - 1 online resource (72 pages)

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

Thesis (Ph.D.)

Includes bibliographical references

Graphs are a rich and fundamental object of study, of interest from both theoretical and applied points of view. This thesis is in two parts and gives a treatment of graphs from two differing points of view, with the goal of doing inference on graphs. The first is a mathematical approach. We create a formal framework to investigate the quality of inference on graphs given partial observations. The proofs we give apply to all graphs without assumptions. In the second part of this thesis, we take on the problem of clustering with the aid of deep neural networks and apply it to the problem of community detection. The results are competitive with the state of the art, even at the information theoretic threshold of recovery of community labels in the stochastic blockmodel.

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

Mode of access: World Wide Web

ISBN: 9780355034097Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Inference on Graphs : = From Probability Methods to Deep Neural Networks.
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020 $a 9780355034097
035 $a (MiAaPQ)AAI10281975
035 $a (MiAaPQ)berkeley:16974
035 $a AAI10281975
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Li, Xiang. $3 683604
245 1 0 $a Inference on Graphs : $b From Probability Methods to Deep Neural Networks.
264 0 $c 2017
300 $a 1 online resource (72 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 Adviser: David Aldous.
502 $a Thesis (Ph.D.) $c University of California, Berkeley $d 2017.
504 $a Includes bibliographical references
520 $a Graphs are a rich and fundamental object of study, of interest from both theoretical and applied points of view. This thesis is in two parts and gives a treatment of graphs from two differing points of view, with the goal of doing inference on graphs. The first is a mathematical approach. We create a formal framework to investigate the quality of inference on graphs given partial observations. The proofs we give apply to all graphs without assumptions. In the second part of this thesis, we take on the problem of clustering with the aid of deep neural networks and apply it to the problem of community detection. The results are competitive with the state of the art, even at the information theoretic threshold of recovery of community labels in the stochastic blockmodel.
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 Computer science. $3 573171
650 4 $a Mathematics. $3 527692
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
690 $a 0984
690 $a 0405
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Berkeley. $b Statistics. $3 1180698
773 0 $t Dissertation Abstracts International $g 78-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10281975 $z click for full text (PQDT)

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