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attention-is-all-you-need.pdf
15 pages · summary ready
What is “attention” in simple terms?
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Attention Is All You Need
Vaswani et al. · 2017 · 15 pages
Summary
This paper introduces the Transformer — a model that processes entire sequences at once using attention, instead of reading word-by-word like earlier RNNs. It trains faster and set new translation records.
Key contributions
- First architecture built entirely on self-attention
- Massively parallel training — days, not weeks
- State-of-the-art BLEU on English↔German translation
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Why is this faster than an RNN?
What are the limitations?
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“Explain multi-head attention”
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Eight parallel attention layers with separate learned projections, concatenated and projected back to d_model = 512.
Subspace projections let heads attend to disjoint representation subspaces, mitigating the averaging effect of single-head attention.
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