Self-Attention
Self-Attention (or scaled dot-product attention) is an attention mechanism that relates different positions of a single sequence to compute a representation of the same sequence, allowing the model to calculate context dynamically.
Frequently Asked Questions
How does self-attention calculate relationships between words?▼
By projecting each word token into three vectors: Query (Q), Key (K), and Value (V). It multiplies Q and K to get a similarity score, scales the result, applies softmax to get attention weights, and multiplies by V.
What is the scaling factor in scaled dot-product attention?▼
It divides the dot products of Query and Key vectors by the square root of their dimension size. This prevents the dot products from growing extremely large in high dimensions, which could push the softmax function into regions with vanishing gradients.
Quick Facts
- CategoryNeural Architectures
- Key ApplicationTransformer encoder-decoder blocks, context calculations, and sequence modeling.
Coverage Trend12 Weeks
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Self-Attention Media Coverage & Intelligence
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