國立虎尾科技大學 |

Deep Learning for Applications in Inverse Modeling, Legislator Analysis, and Computer Vision for Security.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Deep Learning for Applications in Inverse Modeling, Legislator Analysis, and Computer Vision for Security./
Author:	Spell, Gregory.
Description:	1 online resource (190 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798379571238

Deep Learning for Applications in Inverse Modeling, Legislator Analysis, and Computer Vision for Security.
Spell, Gregory.

Deep Learning for Applications in Inverse Modeling, Legislator Analysis, and Computer Vision for Security. - 1 online resource (190 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Duke University, 2023.

Includes bibliographical references

To judiciously use machine learning - particularly deep learning - requires identifying how to extract features from data and effectively leveraging those features to make predictions. This dissertation concerns deep learning methods for three applications: inverse modeling, legislator analysis, and computer vision for security. To address inverse problems, we present a new method, the Mixture Manifold Network, which uses multiple neural backward models in a forward-backward architecture. We experimentally demonstrate that the Mixture Manifold Network performs better than computationally fast generative model baselines, while performance approaching that of computationally slow iterative methods. For legislator modeling, we seek to learn representations that capture legislator attitudes that may not be contained in their voting records. We present a model that instead considers their tweeting behavior, and we use reactions to former President Donald Trump on Twitter as an illustrative example. For computer vision, we address two security-related applications using deep convolutional feature extractors. In the first of these, we leverage domain adaptation with deep object detection for threatening items - such as guns, knives, and blunt objects - in X-ray scans of air passenger luggage. In the second, we apply an occlusion-robust classifier to infrared imagery. For each application above, we describe the datasets for the problem, how the presented methods extract features from that data, and how efficacious predictions are produced from each of our proposed models.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379571238Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

554714
Electronic books.

Deep Learning for Applications in Inverse Modeling, Legislator Analysis, and Computer Vision for Security.
LDR:02953ntm a22003857 4500 001 1148282
005 20240924101840.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379571238
035 $a (MiAaPQ)AAI30311183
035 $a AAI30311183
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Spell, Gregory. $3 1474219
245 1 0 $a Deep Learning for Applications in Inverse Modeling, Legislator Analysis, and Computer Vision for Security.
264 0 $c 2023
300 $a 1 online resource (190 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: Dissertations Abstracts International, Volume: 84-11, Section: B.
500 $a Advisor: Collins, Leslie M.
502 $a Thesis (Ph.D.)--Duke University, 2023.
504 $a Includes bibliographical references
520 $a To judiciously use machine learning - particularly deep learning - requires identifying how to extract features from data and effectively leveraging those features to make predictions. This dissertation concerns deep learning methods for three applications: inverse modeling, legislator analysis, and computer vision for security. To address inverse problems, we present a new method, the Mixture Manifold Network, which uses multiple neural backward models in a forward-backward architecture. We experimentally demonstrate that the Mixture Manifold Network performs better than computationally fast generative model baselines, while performance approaching that of computationally slow iterative methods. For legislator modeling, we seek to learn representations that capture legislator attitudes that may not be contained in their voting records. We present a model that instead considers their tweeting behavior, and we use reactions to former President Donald Trump on Twitter as an illustrative example. For computer vision, we address two security-related applications using deep convolutional feature extractors. In the first of these, we leverage domain adaptation with deep object detection for threatening items - such as guns, knives, and blunt objects - in X-ray scans of air passenger luggage. In the second, we apply an occlusion-robust classifier to infrared imagery. For each application above, we describe the datasets for the problem, how the presented methods extract features from that data, and how efficacious predictions are produced from each of our proposed models.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer science. $3 573171
650 4 $a Statistics. $3 556824
653 $a Deep learning
653 $a Inverse modeling
653 $a Machine learning
653 $a Neural networks
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0984
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Duke University. $b Electrical and Computer Engineering. $3 845695
773 0 $t Dissertations Abstracts International $g 84-11B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30311183 $z click for full text (PQDT)