國立虎尾科技大學 |

Nature of Learning and Learning of Nature.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Nature of Learning and Learning of Nature./
Author:	Garg, Shivam.
Description:	1 online resource (242 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Feedback. -
Online resource:	click for full text (PQDT)
ISBN:	9798381021509

Nature of Learning and Learning of Nature.
Garg, Shivam.

Nature of Learning and Learning of Nature. - 1 online resource (242 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

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

Includes bibliographical references

This thesis explores questions surrounding the foundations of intelligence, both artificial and natural.The first part focuses on the algorithmic and statistical underpinnings of modern machine learning systems. First, we discuss a clean framework for investigating the surprising ability of large language models to learn in-context: the apparent ability to solve new tasks given just a text prompt that provides examples. Further, motivated by concerns around the insatiable data appetite of modern machine learning systems, we discuss the problem of "sample amplification", where we formalize the seemingly naive question of how hard it is to create new data and contrast the hardness of this task to that of learning the data-generating distribution.The second part considers the algorithmic basis of intelligence in nature, specifically in ant colonies and the brain. We examine how arboreal turtle ants solve variants of the shortest path problem without any central control and with minimal computational resources. In the context of the brain, we study how it manages to train its neural network despite its structural limitations. Specifically, we investigate a biologically plausible learning algorithm and contrast it with gradient descent, arguably the only known algorithm for training large-scale artificial neural networks.

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

Mode of access: World Wide Web

ISBN: 9798381021509Subjects--Topical Terms:

1047630
Feedback.
Index Terms--Genre/Form:

554714
Electronic books.

Nature of Learning and Learning of Nature.
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500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Valiant, Gregory;Charikar, Moses;Tan, Li-Yang.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a This thesis explores questions surrounding the foundations of intelligence, both artificial and natural.The first part focuses on the algorithmic and statistical underpinnings of modern machine learning systems. First, we discuss a clean framework for investigating the surprising ability of large language models to learn in-context: the apparent ability to solve new tasks given just a text prompt that provides examples. Further, motivated by concerns around the insatiable data appetite of modern machine learning systems, we discuss the problem of "sample amplification", where we formalize the seemingly naive question of how hard it is to create new data and contrast the hardness of this task to that of learning the data-generating distribution.The second part considers the algorithmic basis of intelligence in nature, specifically in ant colonies and the brain. We examine how arboreal turtle ants solve variants of the shortest path problem without any central control and with minimal computational resources. In the context of the brain, we study how it manages to train its neural network despite its structural limitations. Specifically, we investigate a biologically plausible learning algorithm and contrast it with gradient descent, arguably the only known algorithm for training large-scale artificial neural networks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Feedback. $3 1047630
650 4 $a Neural networks. $3 1011215
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Stanford University. $3 1184533
773 0 $t Dissertations Abstracts International $g 85-06B.
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