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

Foundation Models for the Real World.

Tamkin, Alexander.

Linked to FindBook

Google Book

Amazon

博客來

Foundation Models for the Real World.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Foundation Models for the Real World./
Author:	Tamkin, Alexander.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	179 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Active learning. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30615084
ISBN:	9798380485869

Foundation Models for the Real World.
Tamkin, Alexander.

Foundation Models for the Real World. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 179 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

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

Foundation models are quickly moving from their origins in the lab into real world deployment and use. In this thesis, I discuss two connected lines of research that work towards bridging this gap, so that foundation models can be fruitfully used in real-world settings, e.g. in engineering, medicine, or the sciences. The first is making models more domain-agnostic: while techniques for training foundation models were developed for language and vision domains, we show that simple techniques can generalize these approaches to work across at least twelve different domains. The second is making models more useful in cases of task ambiguity, where the user's desired task may be vague or not-perfectly specified, as is often the case in real-world settings. Here we show how to measure and improve the performance of foundation models under task ambiguity, and explore how models themselves can aid in the process of disambiguating user intent. We close by discussing future directions and the broader outlook of challenges and opportunities ahead.

ISBN: 9798380485869Subjects--Topical Terms:

527777
Active learning.

Foundation Models for the Real World.
LDR:02100nmm a2200373 4500 001 2398305
005 20240812064606.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798380485869
035 $a (MiAaPQ)AAI30615084
035 $a (MiAaPQ)STANFORDgs024vd2209
035 $a AAI30615084
040 $a MiAaPQ $c MiAaPQ
100 1 $a Tamkin, Alexander. $3 3768220
245 1 0 $a Foundation Models for the Real World.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 179 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
500 $a Advisor: Goodman, Noah.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
520 $a Foundation models are quickly moving from their origins in the lab into real world deployment and use. In this thesis, I discuss two connected lines of research that work towards bridging this gap, so that foundation models can be fruitfully used in real-world settings, e.g. in engineering, medicine, or the sciences. The first is making models more domain-agnostic: while techniques for training foundation models were developed for language and vision domains, we show that simple techniques can generalize these approaches to work across at least twelve different domains. The second is making models more useful in cases of task ambiguity, where the user's desired task may be vague or not-perfectly specified, as is often the case in real-world settings. Here we show how to measure and improve the performance of foundation models under task ambiguity, and explore how models themselves can aid in the process of disambiguating user intent. We close by discussing future directions and the broader outlook of challenges and opportunities ahead.
590 $a School code: 0212.
650 4 $a Active learning. $3 527777
650 4 $a Sensors. $3 3549539
650 4 $a Multilingualism. $3 598147
650 4 $a Genomics. $3 600531
650 4 $a Medical imaging. $3 3172799
650 4 $a Speech. $3 530580
650 4 $a Reproducibility. $3 3683754
650 4 $a Particle physics. $3 3433269
650 4 $a Natural language. $3 3562052
650 4 $a Bilingual education. $3 2122778
650 4 $a Education. $3 516579
650 4 $a Genetics. $3 530508
650 4 $a Physics. $3 516296
690 $a 0574
690 $a 0798
690 $a 0282
690 $a 0515
690 $a 0369
690 $a 0605
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 85-04B.
790 $a 0212
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30615084