Transformer Models
Transformer Models is a neural network architecture distinguished by its use of self-attention mechanisms to process sequential data in parallel, enabling highly effective modeling of long-range dependencies in time series prediction and other sequence-based tasks.
5.6K
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2017–2026
4.9K
5.6K
DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2023 · 76.3K citations · Full text
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov et al. · arXiv (Cornell University) · 2020 · 21.6K citations · Full text
Ashish Vaswani, Noam Shazeer, Niki Parmar et al. · 2025 · 6.6K citations · Full text
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Google (United States)
Mountain View, United States
53 h-index
450 publications
324.5K citations

Chinese Academy of Sciences
Beijing, China
45 h-index
314 publications
20.9K citations
Tsinghua University
Beijing, China
44 h-index
255 publications
58.5K citations
University of Chinese Academy of Sciences
Beijing, China
37 h-index
194 publications
14.9K citations
Meta (Israel)
Tel Aviv, Israel
36 h-index
188 publications
22.3K citations

Shanghai Jiao Tong University
Shanghai, China
36 h-index
187 publications
18.6K citations

Peking University
Beijing, China
35 h-index
148 publications
11.2K citations

Zhejiang University
Hangzhou, China
34 h-index
141 publications
11.6K citations
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Repository · 819 publications · 91K citations
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147 publications · 13.1K citations
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Conference · 80 publications · 22K citations
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Conference · 95 publications · 18.3K citations
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98 publications · 5K citations
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