AutoGCN-Toward Generic Human Activity Recognition With Neural Architecture Search

This paper introduces AutoGCN, a generic Neural Architecture Search (NAS) algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks (GCNs). HAR has enjoyed increased attention due to advances in deep learning, increased data availability, and enhanced computational capabilities...

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Vydané v:	IEEE access Ročník 12; s. 39505 - 39516
Hlavní autori:	Tempel, Felix, Ihlen, Espen Alexander F., Strumke, Inga
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Architecture AutoML Buildings Computer architecture Convolutional neural networks Datasets graph convolution networks Human activity recognition Machine learning Neural architecture search Neural networks Search process Task analysis
ISSN:	2169-3536, 2169-3536
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