國立虎尾科技大學 |

Deep Learning Architectures for Novel Problems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Deep Learning Architectures for Novel Problems./
Author:	Petroski Such, Felipe.
Description:	1 online resource (65 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-02.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355408973

Deep Learning Architectures for Novel Problems.
Petroski Such, Felipe.

Deep Learning Architectures for Novel Problems. - 1 online resource (65 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)--Rochester Institute of Technology, 2017.

Includes bibliographical references

With convolutional neural networks revolutionizing the computer vision field it is important to extend the capabilities of neural-based systems to dynamic and unrestricted data like graphs. Doing so not only expands the applications of such systems, but also provide more insight into improvements to neural-based systems.

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

Mode of access: World Wide Web

ISBN: 9780355408973Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Deep Learning Architectures for Novel Problems.
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100 1 $a Petroski Such, Felipe. $3 1184442
245 1 0 $a Deep Learning Architectures for Novel Problems.
264 0 $c 2017
300 $a 1 online resource (65 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Adviser: Raymond Ptucha.
502 $a Thesis (M.S.)--Rochester Institute of Technology, 2017.
504 $a Includes bibliographical references
520 $a With convolutional neural networks revolutionizing the computer vision field it is important to extend the capabilities of neural-based systems to dynamic and unrestricted data like graphs. Doing so not only expands the applications of such systems, but also provide more insight into improvements to neural-based systems.
520 $a Currently most implementations of graph neural networks are based on vertex filtering on fixed adjacency matrices. Although important for a lot of applications, vertex filtering restricts the applications to vertex focused graphs and cannot be effi- ciently extended to edge focused graphs like social networks. Applications of current systems are mostly limited to images and document references.
520 $a Beyond the graph applications, this work also explored the usage of convolutional neural networks for intelligent character recognition in a novel way. Most systems define Intelligent Character Recognition as either a recurrent classification problem or image classification. This achieves great performance in a limited environment but does not generalize well on real world applications. This work defines Intelligent Character Recognition as a segmentation problem which we show to provide many benefits.
520 $a The goal of this work was to explore alternatives to current graph neural networks implementations as well as exploring new applications of such system. This work also focused on improving Intelligent Character Recognition techniques on isolated words using deep learning techniques. Due to the contrast between these to contributions this documents was divided into Part I focusing on the graph work, and Part II focusing on the intelligent character recognition work.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rochester Institute of Technology. $b Computer Engineering. $3 1184443
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10691108 $z click for full text (PQDT)