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Attention (machine learning) - Wikipedia
Attention is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented by "soft" weights assigned to each word in a sentence. More generally, … 展开
Academic reviews of the history of the attention mechanism are provided in Niu et al. and Soydaner.
Predecessors
Selective attention in … 展开Many variants of attention implement soft weights, such as
• fast weight programmers, or fast weight controllers (1992). A "slow" neural network outputs the "fast" weights of another neural network through outer products. The slow network learns … 展开• Dan Jurafsky and James H. Martin (2022) Speech and Language Processing (3rd ed. draft, January 2022), ch. 10.4 Attention and ch. 9.7 Self-Attention Networks: Transformers
• Alex Graves (4 May 2020), Attention and Memory in Deep Learning (video … 展开The attention network was designed to identify high correlations patterns amongst words in a given sentence, assuming that it has learned word correlation patterns from the training data. This correlation is captured as neuronal weights learned during training with 展开
Tasks dealing with language can be cast as a problem of translating general sequences, called seq2seq. One way to build such a machine in 2014 is to graft an attention unit to the recurrent Encoder-Decoder (diagram below). With the advent of Transformers in 2017, … 展开
CC-BY-SA 许可证中的维基百科文本 Transformer (deep learning architecture) - Wikipedia
注意力机制 - 维基百科,自由的百科全书
注意力機制 - 維基百科,自由的百科全書 - zh.wikipedia.org
Transformers Explained Visually (Part 3): Multi-head …
网页2021年1月16日 · In the Transformer, the Attention module repeats its computations multiple times in parallel. Each of these is called an Attention Head. The Attention module splits its Query, Key, and Value parameters …
[1706.03762] Attention Is All You Need - arXiv.org
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All you need to know about ‘Attention’ and …
网页2022年2月14日 · This is a long article that talks about almost everything one needs to know about the Attention mechanism including Self-Attention, Query, Keys, Values, Multi-Head Attention, Masked-Multi Head …
Title: Attention Heads of Large Language Models: A Survey
The Transformer Attention Mechanism
网页2023年1月6日 · Learn how the Transformer model uses self-attention to compute representations of sequences without recurrence or convolutions. Discover the scaled-dot product attention and the multi-head attention …
The Illustrated Transformer – Jay Alammar – Visualizing …
网页2018年6月27日 · Learn how The Transformer, a neural network that uses attention to boost the speed and performance of machine translation, works. See the high-level components, the tensor flows, and the self-attention …
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