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Developing Data Efficient Algorithms in Artificial Intelligence.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Developing Data Efficient Algorithms in Artificial Intelligence./
作者:	Wu, Xian.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	119 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Artificial intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28671116
ISBN:	9798538199969

Developing Data Efficient Algorithms in Artificial Intelligence.
Wu, Xian.

Developing Data Efficient Algorithms in Artificial Intelligence. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 119 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

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

In the past few years there has been an enormous amount of progress in machine learning, and one of the biggest contributing factors, especially for deep learning, is the vast amount of data that we have been able to collect, due to digitization and the internet. Harder and more ambitious problems in general artificial intelligence that that will enable agents to learn on their own and to act autonomously in the environment remain largely open. Initial breakthroughs include training an agent to play a complicated board game, or training agent to drive a car demonstrate that these problems require a lot of data even more data, even more compute than ever before, and possibly more than what we currently have available. This motivates several algorithmic challenges, namely how do we design algorithms that make the best use of the data that is available, and how do we design algorithms that are empirically and theoretically effective on the kinds of data that we often see in practice, for example, data with temporal dependencies and data that follow distributions that are hard to describe. This thesis proposes and analyzes a few algorithmic solutions along this theme, which is an important step to more reliably deploying general artificial intelligence into society.

ISBN: 9798538199969Subjects--Topical Terms:

516317
Artificial intelligence.

Developing Data Efficient Algorithms in Artificial Intelligence.
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