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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Vydáno v:	Applied intelligence (Dordrecht, Netherlands) Ročník 54; číslo 22; s. 11804 - 11844
Hlavní autoři:	Dantas, Pierre Vilar, Sabino da Silva, Waldir, Cordeiro, Lucas Carvalho, Carvalho, Celso Barbosa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.11.2024 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
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