Attention Mechanisms
# Scaled Dot-Product Attention

Introduced by Vaswani et al. in Attention Is All You Need
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**Scaled dot-product attention** is an attention mechanism where the dot products are scaled down by $\sqrt{d_k}$. Formally we have a query $Q$, a key $K$ and a value $V$ and calculate the attention as:

$$ {\text{Attention}}(Q, K, V) = \text{softmax}\left(\frac{QK^{T}}{\sqrt{d_k}}\right)V $$

If we assume that $q$ and $k$ are $d_k$-dimensional vectors whose components are independent random variables with mean $0$ and variance $1$, then their dot product, $q \cdot k = \sum_{i=1}^{d_k} u_iv_i$, has mean $0$ and variance $d_k$. Since we would prefer these values to have variance $1$, we divide by $\sqrt{d_k}$.

Source: Attention Is All You NeedPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Language Modelling | 71 | 8.92% |

Large Language Model | 39 | 4.90% |

Semantic Segmentation | 29 | 3.64% |

Retrieval | 17 | 2.14% |

Prompt Engineering | 14 | 1.76% |

Object Detection | 14 | 1.76% |

Benchmarking | 13 | 1.63% |

Autonomous Driving | 12 | 1.51% |

Question Answering | 12 | 1.51% |