Self-attention deep ConvLSTM with sparse-learned channel dependencies for wearable sensor-based human activity recognition-Reference-Cited by-同舟云学术

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Self-attention deep ConvLSTM with sparse-learned channel dependencies for wearable sensor-based human activity recognition

Published:2024-02 Issue: Volume:571 Page:127157
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Ullah Shan,Pirahandeh Mehdi^ORCID,Kim Deok-Hwan^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference47 articles.

1. A structured self-attentive sentence embedding;Lin,2017

2. Attention is all you need;Vaswani,2017

3. Structured attention networks;Kim,2017

4. A decomposable attention model for natural language inference;Parikh,2016

5. A deep reinforced model for abstractive summarization;Paulus,2017

Cited by 1 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. Weighted voting ensemble of hybrid CNN-LSTM Models for vision-based human activity recognition;Multimedia Tools and Applications;2024-06-08

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