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attention_is_all_you_need.pdfOVERLOAD
Attention Is All You Need
Vaswani et al. · arXiv:1706.03762
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder...
Equation 1: Scaled Dot-Product Attention
Attention(Q,K,V) = softmax(QKT/√dk)V
where queries and keys are of dimension dk, and values are of dimension dv. We compute the dot products of the query with all keys, divide each by √dk...
Equation 2: Multi-Head Attention
MultiHead(Q,K,V) = Concat(head₁,...,headₕ)WO
where headi = Attention(QWQi, KWKi, VWVi)
In this work we employ h = 8 parallel attention layers, or heads. For each of these we use dk = dv = dmodel/h = 64...
We also modify the self-attention sub-layers in the decoder stack to prevent positions from attending to subsequent positions...
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8,000 words · 47 equations · ~3h read↓ scrolling...
explorion · attention_is_all_you_need● LIVE
Reading Progress · 34%
...dominant sequence models based on recurrent networks...
§ Self-Attention
Queries, Keys and Values let every word attend to every other word simultaneously.
...we compute dot products scaled by √dk...
▶ Animation synced
Watch queries flow into the attention matrix →
...multi-head mechanism projects h times in parallel...
The
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Attention Matrix · Layer 6 · Head 3
✦ Animation synced to reading position~4 min · Instant clarity

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