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timezyme.com/zyme/1706.03762v7 Preview
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Attention Is All You Need

Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin·NeurIPS 2017·arXiv:1706.03762v7
Paper in 2 lines~2 min · verification-first
ThesisThe authors propose the Transformer, a network based solely on attention — dispensing with recurrence and convolution entirely.
So whatRemoving sequential computation unlocks far greater parallelization and dramatically faster training — with superior translation quality.
NoveltyThe first transduction model relying entirely on self-attention — no sequence-aligned RNNs or convolution.
Summary confidenceMed

The Procedure

Med

The encoder is 6 identical layers, each with multi-head self-attention and a position-wise feed-forward network, wrapped in residual connections and layer normalization.

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