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Reinforcement Learning for Adaptive Sampling in X-Ray Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reinforcement Learning for Adaptive Sampling in X-Ray Applications./
作者:	Betterton, Jean-Raymond Melingui.
面頁冊數:	1 online resource (110 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
標題:	X-rays. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29755783click for full text (PQDT)
ISBN:	9798357500182

Reinforcement Learning for Adaptive Sampling in X-Ray Applications.
Betterton, Jean-Raymond Melingui.

Reinforcement Learning for Adaptive Sampling in X-Ray Applications. - 1 online resource (110 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2022.

Includes bibliographical references

We propose adaptive sampling algorithms for automating image sampling in scientific x-ray applications. In these applications, we query measurements from various functions of an image in order to estimate it. Since collecting samples is expensive both in terms of time, human resources, and the cost of operating machinery, our goal is to produce autonomous, adaptive sampling methods that attempt to optimize some tradeoff between cost and quality of image estimation, based on information gained from previous measurements. In order to accomplish this, we propose a general methodology that uses reinforcement learning to train autonomous, image-sampling policies that optimize our objective.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798357500182Subjects--Topical Terms:

604695
X-rays.
Index Terms--Genre/Form:

542853
Electronic books.

Reinforcement Learning for Adaptive Sampling in X-Ray Applications.
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502 $a Thesis (Ph.D.)--Stanford University, 2022.
504 $a Includes bibliographical references
520 $a We propose adaptive sampling algorithms for automating image sampling in scientific x-ray applications. In these applications, we query measurements from various functions of an image in order to estimate it. Since collecting samples is expensive both in terms of time, human resources, and the cost of operating machinery, our goal is to produce autonomous, adaptive sampling methods that attempt to optimize some tradeoff between cost and quality of image estimation, based on information gained from previous measurements. In order to accomplish this, we propose a general methodology that uses reinforcement learning to train autonomous, image-sampling policies that optimize our objective.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a X-rays. $3 604695
650 4 $a Computer science. $3 523869
650 4 $a Medical imaging. $3 3172799
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0984
690 $a 0574
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 84-05B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29755783 $z click for full text (PQDT)