國立虎尾科技大學 |

Operationalizing Language for Machine Learning : = Supervision, Interpretation, and Communication.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Operationalizing Language for Machine Learning :/
Reminder of title:	Supervision, Interpretation, and Communication.
Author:	Mu, Jesse L.
Description:	1 online resource (149 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Communication. -
Online resource:	click for full text (PQDT)
ISBN:	9798380484978

Operationalizing Language for Machine Learning : = Supervision, Interpretation, and Communication.
Mu, Jesse L.

Operationalizing Language for Machine Learning :Supervision, Interpretation, and Communication. - 1 online resource (149 pages)

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

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

Includes bibliographical references

Language is a uniquely powerful tool for conceptualizing the world that allows humans to teach, understand, and collaborate with each other. This dissertation surveys three ways in which language can similarly improve the performance and usability of machine learning systems across a variety of tasks and modalities. First, supervision: we use language abstractions to help regularize models and agents for improved performance, even for downstream tasks in vision and reinforcement learning that do not necessarily require language understanding. Second, interpretation: we use language and compositionality to generate explanations of the neurons inside deep neural networks, allowing us to understand and even control model behavior. Finally, communication: we study a class of multiagent signaling games, helping agents learn more robust and interpretable languages by encouraging them to express the generalizations encoded in human languages.

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

Mode of access: World Wide Web

ISBN: 9798380484978Subjects--Topical Terms:

556422
Communication.
Index Terms--Genre/Form:

554714
Electronic books.

Operationalizing Language for Machine Learning : = Supervision, Interpretation, and Communication.
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500 $a Advisor: Andreas, Jacob;Liang, Percy.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a Language is a uniquely powerful tool for conceptualizing the world that allows humans to teach, understand, and collaborate with each other. This dissertation surveys three ways in which language can similarly improve the performance and usability of machine learning systems across a variety of tasks and modalities. First, supervision: we use language abstractions to help regularize models and agents for improved performance, even for downstream tasks in vision and reinforcement learning that do not necessarily require language understanding. Second, interpretation: we use language and compositionality to generate explanations of the neurons inside deep neural networks, allowing us to understand and even control model behavior. Finally, communication: we study a class of multiagent signaling games, helping agents learn more robust and interpretable languages by encouraging them to express the generalizations encoded in human languages.
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538 $a Mode of access: World Wide Web
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650 4 $a Linguistics. $3 557829
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