如BERT（Bidirectional Encoder Representations from Transformers）、GPT（Generative Pretrained Transformer）等。这些模型在Transformer模型的基础上，加入了预训练，多任务等技术，进一步提高了模型的性能，开创了自然语言处理的新篇章。论文最后作者也提出了Transforme
简介mechanism , [‘mek(ə)nɪz(ə)m]. 最近两年,注意力机制(Attention Mechanism )被广泛使用在自然语言处理、图像识别及语音识别等各种不同类型的深度学习任务中,是一个值得关注与深入了解的核心技术。 人的注意力机制: 拿到一篇文章时, 会重点关注标题和段落首句, 期望快速得到关键信息. 人群中看到心动...
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...
the increased computational complexity, stemming primarily from the self-attention mechanism, parallels the manner in which convolution operations constrain the capabilities and speed of convolutional neural networks (CNNs). The self-attention algorithm, specifically the matrix-matrix multiplication...
In this research, we propose the sequence pair feature extractor, inspired by Bidirectional Encoder Representations from Transformers (BERT)'s sentence pair task, to obtain a dynamic representation of a pair of ECGs. We also propose using the self-attention mechanism of the transformer to draw an...
但是了解注意力机制的发展过程,可以更好的帮助自注意力机制的理解。因此,推荐感兴趣的读者,读一下这篇专门介绍注意力机制发展史的论文《Attention Mechanism in Neural Networks: Where it Comes and Where it Goes》(https://arxiv.org/pdf/2204.13154)。
Transformers have sprung up in the field of computer vision. In this work, we explore whether the core self-attention module in Transformer is the key to achieving excellent performance in image recognition. To this end, we build an attention-free network called sMLPNet...
traits from multiple "parents" through weighted summation. Stronger relationships dominate; weaker ones fade. This crazy yet efficient "breeding" compresses linguistic structure into dense vector spaces, a process conceptually equivalent to parsing, understanding, and generation in one unified mechanism. ...
This requires moving the position encoding into the attention mechanism (which is detailed in the paper). One benefit is that the resulting transformer will likely generalize much better to sequences of unseen length. 8.4 Sparse transformers Sparse transformers tackle the problem of quadratic memory ...
The combination of Transformers' capabilities with the energy efficiency of SNNs offers a compelling opportunity. This paper addresses the challenge of adapting the self-attention mechanism of Transformers to the spiking paradigm by introducing a novel approach: Accurate Addition-Only Spiking Self-...