既然我们已经基本掌握了点积的计算方法,那么就可以开始深入研究注意力机制(attention)了,特别是自注意力机制(self-attention mechanism)。使用自注意力机制使模型能够确定每个单词的重要性,而不管它与其他单词的“物理”距离是多少。这使得模型能够根据每个单词的上下文相关性(contextual relevance)做出比较明智的决策,从而更...
注意力机制(Attention Mechanism) 自注意力机制 多头自注意力机制 位置编码(Positional Encoding) 前馈神经网络(Feed-Forward Neural Network,FFNN) 层归一化(Layer Normalization) 残差连接(Residual Connection) 总结 概述 "Attention Is All You Need"是一篇于2017年发表的重要论文,由Google的研究者撰写。这篇论文提出...
In doing so, it produces an attention output for the word under consideration." (3) attention mechanism in Attention is all you need https://arxiv.org/abs/1706.03762 (transformers) An attention function can be described as mapping a query and a set of key-value pairs to an output, where...
个人感觉和直观理解 Intuition 既然我们已经基本掌握了点积的计算方法,那么就可以开始深入研究注意力机制(attention)了,特别是自注意力机制(self-attention mechanism)。使用自注意力机制使模型能够确定每个单词的重要性,而不管它与其他单词的“物理”距离是多少。这使得模型能够根据每个单词的上下文相关性(contextual relevanc...
第一部分:注意力机制(Attention Mechanism)浅谈 1. 注意力机制的由来,解决了什么问题? 早期在解决机器翻译这一类序列到序列(Sequence to Sequence)的问题时,通常采用的做法是利用一个编码器(Encoder)和一个解码器(Decoder)构建端到端的神经网络模型,但是基于编码解码的神经网络存在两个问题,拿机器翻译举例: ...
所谓硬查询，它的过程类似于python中的dict。本质上是用一个q，在一个k-v对里查询v，如果q等于某个...
While modeling a text sequence, it is also important to represent the intra relationship of positions of various elements in the sequence. Such modelling is often called self-attention or intra-attention. Transformers use self-attention mechanism alone in order to represent &...
因果关系自回归模型:在自回归模型(如GPT系列)中,因果注意力机制(causal attention mechanism)通过限制每个元素只能与之前的元素进行交互,从而隐含地引入了位置信息。尽管显式的位置编码可以提高模型性能,但一些研究表明,即使没有显式位置编码,这类模型也能够学习到一定的位置信息。 特定的非序列任务:对于一些不依赖于元素...
in Sect. "Swin-transformer-based feature extraction”. Secondly, we describe the feature fusion network based on ASA (including attention mechanism and position encoding) in Sect. "Adaptive sparse attention-based feature fusion”. Finally, in Sect. "Head and training loss", we present a detailed...
We will first focus on the Transformer attention mechanism in this tutorial and subsequently review the Transformer model in a separate one. In this tutorial, you will discover the Transformer attention mechanism for neural machine translation. After completing this tutorial, you will know: How the ...