東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Scalable Representation Learning: On...

Le Loh, Charlotte Chang ,

Linked to FindBook

Google Book

Amazon

博客來

Scalable Representation Learning: On Data-Scarcity, Uncertainty and Symmetry /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Scalable Representation Learning: On Data-Scarcity, Uncertainty and Symmetry // Charlotte Chang Le Loh.
Author:	Le Loh, Charlotte Chang ,
Description:	1 electronic resource (231 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
Contained By:	Dissertations Abstracts International86-08B.
Subject:	Photonics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31851394
ISBN:	9798304948197

Scalable Representation Learning: On Data-Scarcity, Uncertainty and Symmetry /
Le Loh, Charlotte Chang ,

Scalable Representation Learning: On Data-Scarcity, Uncertainty and Symmetry /Charlotte Chang Le Loh. - 1 electronic resource (231 pages)

Source: Dissertations Abstracts International, Volume: 86-08, Section: B.

Deep learning has experienced remarkable success in recent years, leading to significant advancements in various fields such as vision, natural language generation, complex game play, as well as solving difficult scientific problems such as predicting protein folding. Despite these successes, traditional deep learning faces fundamental challenges limiting their scalability and effectiveness. These challenges include the necessity for extensive labeled datasets, the lack of trustworthiness due to model overconfidence, and difficulties in generalizing to new, unseen data. In this thesis, our primary goal is to tackle these issues by introducing novel tools and methods that augment traditional deep learning. We explore various strategies for solving the main bottlenecks of traditional deep learning, which includes incorporating prior known symmetries and inductive biases of the problem, utilizing Bayesian and ensemble methods, and leveraging abundance of unlabeled data in a representation learning framework. We discuss and demonstrate practical applications of these novel tools in diverse domains including vision, photonics, material science and neuroscience.

English

ISBN: 9798304948197Subjects--Topical Terms:

526247
Photonics.
Subjects--Index Terms:

Deep learning

Scalable Representation Learning: On Data-Scarcity, Uncertainty and Symmetry /
LDR:02511nmm a22004093i 4500 001 2432646
005 20250731101904.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798304948197
035 $a (MiAaPQD)AAI31851394
035 $a (MiAaPQD)MIT17211156614
035 $a AAI31851394
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Le Loh, Charlotte Chang , $e author. $3 3821949
245 1 0 $a Scalable Representation Learning: On Data-Scarcity, Uncertainty and Symmetry / $c Charlotte Chang Le Loh.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (231 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: 86-08, Section: B.
500 $a Advisors: Soljacic, Marin.
502 $b Ph.D. $c Massachusetts Institute of Technology $d 2024.
520 $a Deep learning has experienced remarkable success in recent years, leading to significant advancements in various fields such as vision, natural language generation, complex game play, as well as solving difficult scientific problems such as predicting protein folding. Despite these successes, traditional deep learning faces fundamental challenges limiting their scalability and effectiveness. These challenges include the necessity for extensive labeled datasets, the lack of trustworthiness due to model overconfidence, and difficulties in generalizing to new, unseen data. In this thesis, our primary goal is to tackle these issues by introducing novel tools and methods that augment traditional deep learning. We explore various strategies for solving the main bottlenecks of traditional deep learning, which includes incorporating prior known symmetries and inductive biases of the problem, utilizing Bayesian and ensemble methods, and leveraging abundance of unlabeled data in a representation learning framework. We discuss and demonstrate practical applications of these novel tools in diverse domains including vision, photonics, material science and neuroscience.
546 $a English
590 $a School code: 0753
650 4 $a Photonics. $3 526247
650 4 $a Crystals. $3 524190
650 4 $a Symmetry. $3 536815
650 4 $a Engineering. $3 586835
650 4 $a Computer science. $3 523869
653 $a Deep learning
653 $a Labeled datasets
690 $a 0984
690 $a 0800
690 $a 0537
710 2 $a Massachusetts Institute of Technology. $b Department of Electrical Engineering and Computer Science. $e degree granting institution. $3 3821948
720 1 $a Soljacic, Marin $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-08B.
790 $a 0753
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31851394