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12 highlights · 4 notes
Ch. 3 of 8
AT
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.

Architecture
Key claim
ME
My margin note · Ch. 22h ago · synced
Insight

Multi-head attention lets the model attend to information from different representation subspaces. Each head learns a distinct projection — together they capture syntactic and semantic patterns in parallel.

Pinned quote
Exam-worthy
p. 12

“Attention is all you need — recurrence and convolutions are not required for state-of-the-art sequence transduction.”

Research — TransformersUpdated 5 min ago · 4 backlinks
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Goal: understand why attention beats RNNs for long-range dependencies — prep for Friday seminar.

Read Section 3 — Model architecture
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Write 200-word summary in own words
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Compare encoder-decoder attention vs self-attention — could be a great blog post. Reference Bahdanau (2014) as the historical anchor.

Lecture notes — Mar 12CS 224N · Stanford
Auto-synced

Prof. Tanaka emphasized that positional encoding is critical since attention has no inherent order. Sin/cos works because relative positions become linear functions of the encoding vector.

PE(pos, 2i) = sin(pos / 10000^(2i/d))
TransformersVaswani et al. · 2017
Self-Attention
Q · K · V
Scaled dot-product
Multi-Head
8 heads
Parallel
Positional Encoding
sin / cos
No order bias
Feed-Forward
2 layers
ReLU

What does the paper say about scaling self-attention beyond 512 tokens, and how does it compare to Longformer's sliding window?

3 sources
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Vanilla self-attention has O(n²) memory and compute complexity, which makes contexts beyond 512 tokens impractical on standard GPUs. Three approaches dominate the literature:

Longformer — sliding window of 512 + global tokens → O(n)
Performer — kernel approximation with random features
FlashAttention — exact, but IO-aware tiling for speed

From your highlights: “the dot-product becomes the bottleneck above 1k tokens” — matches Vaswani §3.2.

Vaswani · p.6
Beltagy · p.3
Dao · p.2
My notes · Ch. 3
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Attention Is All You Need
NeurIPS 2017 — 12 pages
V
Vaswani
BERT Pre-training
NAACL 2019 — 16 pages
D
Devlin
Reviewed
Drop here
Attention Is All You Need
NeurIPS 2017 — 12 pages
V
Vaswani
What did the author say about transformers?
Vaswani et al.Author · Google Brain
Attention Is All You NeedPDF · 12 pages
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Attention Is All You Need.pdf
Shared · 1.8 MB
TypePDF Document
AuthorsVaswani et al.
ModifiedYesterday
Lit Review Session
Apr 22 · 45 min
Summary4 papers reviewed
Notes12 highlights
DateApr 22, 2026
Note · Transformer Arch
Updated 2h ago
Linked to3 sources
TagsNLP · ML
Backlinks5 notes
Highlight · "self-attention"
Page 4 · Yellow
SourceVaswani 2017
ColorYellow
Linked2 notes
AI Chat · "Explain heads"
Today · 4:32 PM
Sources2 papers
Citations5 pages
Saved toNote · TA
Citation · Vaswani et al.
NeurIPS 2017
Cited in3 notes
FormatBibTeX
Year2017
Quiz · Chapter 4
12 cards
Score9 / 12
Reviewed2 days ago
SourceNote · TA
Mindmap · Attention Family
Auto-built
Nodes24
Sources5 papers
Updated1h ago
Flashcards · Encoder
18 cards
Mastered11 / 18
SourceNote · TA
Due4 cards
BERT.pdf
Shared · 2.1 MB
TypePDF Document
AuthorsDevlin et al.
Year2018
Highlight · "masked LM"
Page 3 · Pink
SourceDevlin 2018
ColorPink
Linked1 note
Note · Pre-training methods
Updated yesterday
Linked to4 sources
TagsNLP · pretraining
Backlinks3 notes
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Attention is All You NeedVaswani et al. · p.42 / 156
ED
SK
+2
ED
Edward
Key insight — link to Section 5
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THE WORK DOCS
Recall improved 18% over baselineVaswani p.42, but precision dropped slightlyDevlin p.7.
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— Vaswani et al., p.42
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