A comprehensive review of model compression techniques in machine learning

This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things (IoT) systems. By systematical...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Jg. 54; H. 22; S. 11804 - 11844
Hauptverfasser:	Dantas, Pierre Vilar, Sabino da Silva, Waldir, Cordeiro, Lucas Carvalho, Carvalho, Celso Barbosa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2024 Springer Nature B.V
Schlagworte:	Artificial Intelligence Complexity Computer Science Computing time Edge computing Efficiency Internet of Things Machine learning Machines Manufacturing Mechanical Engineering Optimization Processes System effectiveness Architectural innovations Technological evolution in machine learning Lightweight design approaches Computational efficiency Model generalization Neural network compression
ISSN:	0924-669X, 1573-7497
Online-Zugang:	Volltext
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