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Advancements in knowledge distillati...

Pedrycz, Witold.

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Advancements in knowledge distillation: towards new horizons of intelligent systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancements in knowledge distillation: towards new horizons of intelligent systems/ edited by Witold Pedrycz, Shyi-Ming Chen.
other author:	Pedrycz, Witold.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	viii, 232 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation -- A Geometric Perspective on Feature-Based Distillation -- Knowledge Distillation Across Vision and Language -- Knowledge Distillation in Granular Fuzzy Models by Solving Fuzzy Relation Equations -- Ensemble Knowledge Distillation for Edge Intelligence in Medical Applications -- Self-Distillation with the New Paradigm in Multi-Task Learning -- Knowledge Distillation for Autonomous Intelligent Unmanned System.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-031-32095-8
ISBN:	9783031320958

Advancements in knowledge distillation: towards new horizons of intelligent systems
Advancements in knowledge distillation: towards new horizons of intelligent systems[electronic resource] /edited by Witold Pedrycz, Shyi-Ming Chen. - Cham :Springer International Publishing :2023. - viii, 232 p. :ill. (chiefly col.), digital ;24 cm. - Studies in computational intelligence,v. 11001860-9503 ;. - Studies in computational intelligence ;v. 1100..

Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation -- A Geometric Perspective on Feature-Based Distillation -- Knowledge Distillation Across Vision and Language -- Knowledge Distillation in Granular Fuzzy Models by Solving Fuzzy Relation Equations -- Ensemble Knowledge Distillation for Edge Intelligence in Medical Applications -- Self-Distillation with the New Paradigm in Multi-Task Learning -- Knowledge Distillation for Autonomous Intelligent Unmanned System.

The book provides a timely coverage of the paradigm of knowledge distillation-an efficient way of model compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teacher model. The book covers a variety of training schemes, teacher-student architectures, and distillation algorithms. The book covers a wealth of topics including recent developments in vision and language learning, relational architectures, multi-task learning, and representative applications to image processing, computer vision, edge intelligence, and autonomous systems. The book is of relevance to a broad audience including researchers and practitioners active in the area of machine learning and pursuing fundamental and applied research in the area of advanced learning paradigms.

ISBN: 9783031320958

Standard No.: 10.1007/978-3-031-32095-8doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 006.3

Advancements in knowledge distillation: towards new horizons of intelligent systems
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245 0 0 $a Advancements in knowledge distillation: towards new horizons of intelligent systems $h [electronic resource] / $c edited by Witold Pedrycz, Shyi-Ming Chen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a viii, 232 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1100
505 0 $a Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation -- A Geometric Perspective on Feature-Based Distillation -- Knowledge Distillation Across Vision and Language -- Knowledge Distillation in Granular Fuzzy Models by Solving Fuzzy Relation Equations -- Ensemble Knowledge Distillation for Edge Intelligence in Medical Applications -- Self-Distillation with the New Paradigm in Multi-Task Learning -- Knowledge Distillation for Autonomous Intelligent Unmanned System.
520 $a The book provides a timely coverage of the paradigm of knowledge distillation-an efficient way of model compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teacher model. The book covers a variety of training schemes, teacher-student architectures, and distillation algorithms. The book covers a wealth of topics including recent developments in vision and language learning, relational architectures, multi-task learning, and representative applications to image processing, computer vision, edge intelligence, and autonomous systems. The book is of relevance to a broad audience including researchers and practitioners active in the area of machine learning and pursuing fundamental and applied research in the area of advanced learning paradigms.
650 0 $a Artificial intelligence. $3 516317
650 0 $a Information technology $x Technological innovations. $3 859080
650 0 $a Knowledge management $x Data processing. $3 608802
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Pedrycz, Witold. $3 899642
700 1 $a Chen, Shyi-Ming. $3 1067501
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 1100. $3 3634337
856 4 0 $u https://doi.org/10.1007/978-3-031-32095-8
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)