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Learning by Watching and Learning by...

Gordon, Daniel.

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Learning by Watching and Learning by Doing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning by Watching and Learning by Doing./
Author:	Gordon, Daniel.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	199 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27997699
ISBN:	9798664707137

Learning by Watching and Learning by Doing.
Gordon, Daniel.

Learning by Watching and Learning by Doing. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 199 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--University of Washington, 2020.

This item must not be sold to any third party vendors.

When we are babies, we learn how to see by watching how the world changes and by interacting with it. Can we use these same signals to train vision models? In this thesis, we outline several works which use these paradigms as a basis for learning algorithms. First, we explore learning by watching in which video data is directly used to learn about the visual world. Second, we tackle multiple challenging tasks in embodied environments in which agents learn by interacting with their surroundings.

ISBN: 9798664707137Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Computer vision

Learning by Watching and Learning by Doing.
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245 1 0 $a Learning by Watching and Learning by Doing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 199 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
500 $a Advisor: Farhadi, Ali;Fox, Dieter.
502 $a Thesis (Ph.D.)--University of Washington, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a When we are babies, we learn how to see by watching how the world changes and by interacting with it. Can we use these same signals to train vision models? In this thesis, we outline several works which use these paradigms as a basis for learning algorithms. First, we explore learning by watching in which video data is directly used to learn about the visual world. Second, we tackle multiple challenging tasks in embodied environments in which agents learn by interacting with their surroundings.
590 $a School code: 0250.
650 4 $a Computer science. $3 523869
650 4 $a Robotics. $3 519753
653 $a Computer vision
653 $a Deep learning
653 $a Machine learning
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