srch

13.03.2020 · We introduce a new way of learning to encode position information for non-recurrent models, such as Transformer models. Unlike RNN and LSTM, which contain inductive bias by loading the input tokens sequentially, non-recurrent models are less sensitive to position. The main reason is that position information among input units is not inherently encoded, i.e., the …

03.06.2021 · Position Encoding. Since an RNN implements a loop where each word is input sequentially, it implicitly knows the position of each word. However, …

In the Transformer architecture, positional encoding is used to give the order context to the non-recurrent architecture of multi-head attention ...

19.08.2019 · Transformer with Python and TensorFlow 2.0 – Encoder & Decoder. In one of the previous articles, we kicked off the Transformer architecture. Transformer is a huge system with many different parts. They are relying on the same principles like Recurrent Neural Networks and LSTM s, but are trying to overcome their shortcomings.

What a positional encoder does is to get help of the cyclic nature of sin(x) and cos(x) functions to return information of the position of a word in a sentence.

We introduce a new way of learning to encode position information for non-recurrent models, such as Transformer models. Unlike RNN and LSTM, ...

A positional encoding is a finite dimensional representation of the location or “position” of items in a sequence. Given some sequence A = [a_0, …, a_{n-1}], ...

30.03.2020 · All input and output tokens to Encoder/Decoder are converted to vectors using learned embeddings. These input embeddings are then passed to Positional Encoding. Positional Encoding. The Transformer’s architecture does not contain any recurrence or convolution and hence has no notion of word order.

But the embeddings do not encode the relative position of tokens in a sentence. So after adding the positional encoding, tokens will be closer to each other ...

Positional Encoding和embedding具有同样的维度 ，因此这两者可以直接相加。 在本文中，作者们使用了不同频率的正弦和余弦函数来作为位置编码： 开始看到这两个式子，会觉得很莫名其妙，这个sin，cos，10000都是从哪冒出来的？

The Transformer outperforms the Google Neural Machine Translation model ... word in each position flows through its own path in the encoder.

... flows through the Transformer's encoder/decoder stack, The model itself doesn't have any sense of position/order for each word.

Learning to Encode Position for Transformer with Continuous Dynamical Model. Xuanqing Liu 1 Hsiang-Fu Yu 2 Inderjit S. Dhillon 3 2 Cho-Jui Hsieh 1. Abstract.

Unlike sequential algorithms like `RNN`s and `LSTM`, transformers don't have a mechanism built in to capture the relative positions of words ...

如上公式可知，transformer的encoding方式是天然丢失方向信息的。. 解决方案如下。. 核心公式在于. 原始的self-attention是只有Qt*Kj，这里把位置信息也跟Qt相乘了。. 且Rt-j的设定方式也决定它能反映出位置信息。. 假设t=5，j分别为0，10，则t-j分别为5和-5。. 已知sin (-x ...