Enhancing Efficiency in HAR Models: NAS Meets Pruning-Reference-Cited by-同舟云学术

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Enhancing Efficiency in HAR Models: NAS Meets Pruning

Published:2024-03-11 Issue: Volume: Page:
ISSN:
Container-title:2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
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Author:

Zhou Yexu¹,King Tobias¹,Huang Yiran¹,Zhao Haibin¹,Riedel Till¹,Röddiger Tobias¹,Beigl Michael¹

Affiliation:

1. Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10502396/10502378/10502894.pdf?arnumber=10502894

Reference21 articles.

1. ALAE-TAE-CutMix+: Beyond the State-of-the-Art for Human Activity Recognition Using Wearable Sensors

2. TinyHAR: A Lightweight Deep Learning Model Designed for Human Activity Recognition

3. DeepSense

4. Attend and Discriminate

5. Ifconvtransformer: A framework for human activity recognition using imu fusion and convtransformer;Zhang

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Survey on Wearable Human Activity Recognition: Innovative Pipeline Development for Enhanced Research and Practice;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Deep Neural Network Pruning with Progressive Regularizer;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. ExTea: An Evolutionary Algorithm-Based Approach for Enhancing Explainability in Time-Series Models;Lecture Notes in Computer Science;2024

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