Position Embeddings
# Absolute Position Encodings

Introduced by Vaswani et al. in Attention Is All You Need
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#### Usage Over Time

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Categories

**Absolute Position Encodings** are a type of position embeddings for [Transformer-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:

$$ \text{PE}\left(pos, 2i\right) = \sin\left(pos/10000^{2i/d_{model}}\right) $$

$$ \text{PE}\left(pos, 2i+1\right) = \cos\left(pos/10000^{2i/d_{model}}\right) $$

where $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\pi$ to $10000 \dot 2\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\text{PE}_{pos+k}$ can be represented as a linear function of $\text{PE}_{pos}$.

Image Source: D2L.ai

Source: Attention Is All You NeedPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Language Modelling | 50 | 6.73% |

Large Language Model | 26 | 3.50% |

Question Answering | 20 | 2.69% |

Retrieval | 20 | 2.69% |

Semantic Segmentation | 17 | 2.29% |

In-Context Learning | 14 | 1.88% |

Text Generation | 14 | 1.88% |

Decision Making | 13 | 1.75% |

Image Segmentation | 11 | 1.48% |