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2024-06-27 22:38:06
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2024-06-28 12:46:28
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title
description
image
site name
author
updated
2026-01-02 11:43:14
raw text
Linear Transformers Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention Angelos Katharopoulos 1,2 Apoorv Vyas 1,2 Nikolaos Pappas 3 François Fleuret 1,2 1 Idiap Research Institute 2 École Polytechnique Fédérale de Lausanne 3 University of Washington ICML 2020 Paper Colab Code Docs Video Slides Transformers achieve remarkable performance in several tasks but due to their quadratic complexity, with respect to the input's length, they are prohibitively slow for very long sequences. To address this limitation, we express the self-attention as a linear dot-product of kernel feature maps and make use of the associativity property of matrix products to reduce the comp...
redirect type
0 (-)
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1 (English)
category id
Other [en] (231)
index version
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2026-01-02 11:43:14
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