Attention-based Classification
Attention-Based Classification is a classification framework that employs attention mechanisms to selectively emphasize informative temporal segments or features within time series data, thereby improving model interpretability and predictive accuracy by differentiating relevant from irrelevant input regions.
453
Publications
32.6K
Citations
1.9K
Authors
578
Institutions
Publications per year
2017–2026
425
Parents
453
Hierarchical Attention Networks for Document Classification
Zichao Yang, Diyi Yang, Chris Dyer et al. · 2016 · 4.8K citations · Full text
LSTM Fully Convolutional Networks for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi et al. · IEEE Access · 2017 · 1.4K citations · Full text
Interactive Attention Networks for Aspect-Level Sentiment Classification
Dehong Ma, Sujian Li, Xiaodong Zhang et al. · 2017 · 1K citations · Full text
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578

Chinese Academy of Sciences
Beijing, China
13 h-index
46 publications
2.1K citations
Tsinghua University
Beijing, China
11 h-index
27 publications
2.6K citations

Harbin Institute of Technology
Harbin, China
10 h-index
36 publications
1.2K citations

Peking University
Beijing, China
10 h-index
25 publications
5.3K citations
University of Chinese Academy of Sciences
Beijing, China
9 h-index
22 publications
796 citations

Northwestern Polytechnical University
Xi'an, China
9 h-index
32 publications
1.9K citations

Zhejiang University
Hangzhou, China
7 h-index
18 publications
802 citations

Beijing University of Posts and Telecommunications
Beijing, China
6 h-index
13 publications
317 citations
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Repository · 33 publications · 1.4K citations
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15 publications · 1.7K citations
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13 publications · 2.1K citations
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Conference · 11 publications · 1.4K citations
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11 publications · 329 citations
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