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Attention is All You NeedVaswani et al. · p.42 / 156
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Attention Is All You NeedVaswani et al. · NeurIPS 2017 · p. 3
The Transformer relies entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution, enabling significantly more parallelization.
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Attention Is All You Need
NeurIPS 2017 — 12 pages
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Attention Is All You Need
NeurIPS 2017 — 12 pages
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Attention Is All You NeedPDF · 12 pages
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TypePDF Document
AuthorsVaswani et al.
ModifiedYesterday
Apr 22 · 45 min
Summary4 papers reviewed
Notes12 highlights
DateApr 22, 2026
Updated 2h ago
Linked to3 sources
TagsNLP · ML
Backlinks5 notes
Page 4 · Yellow
SourceVaswani 2017
ColorYellow
Linked2 notes
Today · 4:32 PM
Sources2 papers
Citations5 pages
Saved toNote · TA
NeurIPS 2017
Cited in3 notes
FormatBibTeX
Year2017
12 cards
Score9 / 12
Reviewed2 days ago
SourceNote · TA
18 cards
Mastered11 / 18
SourceNote · TA
Due4 cards
Shared · 2.1 MB
TypePDF Document
AuthorsDevlin et al.
Year2018
Page 3 · Pink
SourceDevlin 2018
ColorPink
Linked1 note
Updated yesterday
Linked to4 sources
TagsNLP · pretraining
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