St. Clair College - Windsor/South Campus

Distributed Learning Systems with First-Order Methods

Ce Zhang, author.

Boston [Massachusetts] : Now Publishing Inc., 2020.

1 PDF (106 pages) : color illustrations.

Scalable and efficient distributed learning is one of the main driving forces behind the recent rapid advancement of machine learning and artificial intelligence. One prominent feature of this development is that recent progress has been made by researchers in two communities: (1) the system community such as database, data management, and distributed systems, and (2) the machine learning and mathematical optimization community. The interaction and knowledge sharing between these two communities has led to the rapid development of new distributed learning systems and theory. This monograph provides a brief introduction to three distributed learning techniques that have recently been developed: lossy communication compression, asynchronous communication, and decentralized communication. These have significant impact on the work in both the system and machine learning and mathematical optimization communities but to fully realize the potential, it is essential they understand the whole picture. This monograph provides the bridge between the two communities. The simplified introduction to the essential aspects of each community enables researchers to gain insights into the factors influencing both. The monograph provides students and researchers the groundwork for developing faster and better research results in this dynamic area of research. Provided by publisher.

Available

Electronic EbookElectronic Ebook

1 copy available at St. Clair College - Windsor/South Campus

LC Call No:

TJ211.49 .R63 2020eb

Dewey Class No:

629.8/924019 23

Author:

Ce Zhang, author.

Title:

Distributed Learning Systems with First-Order Methods / Ji Liu; Ce Zhang.

Physical:

1 PDF (106 pages) : color illustrations.

ContentType:

text rdacontent

MediaType:

electronic isbdmedia

CarrierType:

online resource rdacarrier

Series:

Foundations and trends in information systems, 2331-124X ; 4:2

BibliogrphyNote:

Includes bibliographical references (pages 88-106).

Summary:

Scalable and efficient distributed learning is one of the main driving forces behind the recent rapid advancement of machine learning and artificial intelligence. One prominent feature of this development is that recent progress has been made by researchers in two communities: (1) the system community such as database, data management, and distributed systems, and (2) the machine learning and mathematical optimization community. The interaction and knowledge sharing between these two communities has led to the rapid development of new distributed learning systems and theory. This monograph provides a brief introduction to three distributed learning techniques that have recently been developed: lossy communication compression, asynchronous communication, and decentralized communication. These have significant impact on the work in both the system and machine learning and mathematical optimization communities but to fully realize the potential, it is essential they understand the whole picture. This monograph provides the bridge between the two communities. The simplified introduction to the essential aspects of each community enables researchers to gain insights into the factors influencing both. The monograph provides students and researchers the groundwork for developing faster and better research results in this dynamic area of research. Provided by publisher.

AE:PersName:

Zhang, Qiaoning, author.

AE:PersName:

You, Sangseok, author.

AE:PersName:

Kim, Sangmi, author.

AE:PersName:

Esterwood, Connor, author.

AE:PersName:

Alahmad, Rasha, author.

AE:CorpName:

Now Publications, publisher.

AE:CorpName:

IEEE Xplore (Online Service), distributor.

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    $b Name of prod./pub./dist./man.  Now Publishing Inc.,
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505 505 0   $a 505  1. Introduction -- 2. Literature review -- 3. Thrust area. 1. Human personality and human€€“robot interaction -- 4. Thrust area 2. Robot personality and human€€“robot interaction -- 5. Thrust area 3. Robot and human personality similarities and differences -- 6. Thrust area 4. Factors impacting robot personality -- 7. Major findings and a way forward -- References.
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520 Summary   $a Summary, etc. note  Scalable and efficient distributed learning is one of the main driving forces behind the recent rapid advancement of machine learning and artificial intelligence. One prominent feature of this development is that recent progress has been made by researchers in two communities: (1) the system community such as database, data management, and distributed systems, and (2) the machine learning and mathematical optimization community. The interaction and knowledge sharing between these two communities has led to the rapid development of new distributed learning systems and theory. This monograph provides a brief introduction to three distributed learning techniques that have recently been developed: lossy communication compression, asynchronous communication, and decentralized communication. These have significant impact on the work in both the system and machine learning and mathematical optimization communities but to fully realize the potential, it is essential they understand the whole picture. This monograph provides the bridge between the two communities. The simplified introduction to the essential aspects of each community enables researchers to gain insights into the factors influencing both. The monograph provides students and researchers the groundwork for developing faster and better research results in this dynamic area of research.
    $c   Provided by publisher.
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588 588   $a 588  Description based on PDF viewed May 20, 2020.
650 Subj. $a Topical heading  Human-robot interactions.
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    $e Relator  author.
700 AE:PersName 1   $a Personal name  Esterwood, Connor,
    $e Relator  author.
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