Position Embeddings

# Absolute Position Encodings

Introduced by Vaswani et al. in Attention Is All You Need

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 Need

#### Papers

Paper Code Results Date Stars

Machine Translation 49 6.90%
Language Modelling 46 6.48%
Image Classification 27 3.80%
Object Detection 27 3.80%
Semantic Segmentation 24 3.38%
Time Series 18 2.54%
Text Generation 16 2.25%
Speech Recognition 15 2.11%
Question Answering 14 1.97%

#### Components

Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign