Positional Encoding
Positional Encoding is a deterministic or learned vector representation added to token embeddings in sequence models to provide information about the relative or absolute positions of tokens within a sequence, distinguishing it from other embedding components by explicitly encoding sequential order in contexts where such information is otherwise absent.
317
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35.6K
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1.1K
Authors
389
Institutions
Publications per year
2017–2026
206
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SegFormer: Simple and Efficient Design for Semantic Segmentation with\n Transformers
Enze Xie, Wenhai Wang, Zhiding Yu et al. · arXiv (Cornell University) · 2021 · 3.3K citations · Full text
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context
Zihang Dai, Zhilin Yang, Yiming Yang et al. · 2019 · 3.1K citations · Full text
Are Transformers Effective for Time Series Forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 2023 · 2.6K citations · Full text
RoFormer: Enhanced transformer with Rotary Position Embedding
Jianlin Su, Murtadha Ahmed, Yu Lu et al. · Neurocomputing · 2023 · 1.4K citations
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University College London
London, United Kingdom
8 h-index
10 publications
1.2K citations

Howard Hughes Medical Institute
Chevy Chase, United States
8 h-index
16 publications
1.5K citations
Tsinghua University
Beijing, China
5 h-index
12 publications
356 citations
National University of Singapore
Singapore, Singapore
5 h-index
11 publications
807 citations

Centre National de la Recherche Scientifique
Paris, France
5 h-index
17 publications
3.6K citations

Peking University
Beijing, China
5 h-index
7 publications
433 citations
Google (United States)
Mountain View, United States
5 h-index
25 publications
6.3K citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
12 publications
651 citations
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Repository · 49 publications · 6.9K citations
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Conference · 9 publications · 2.7K citations
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