Source: Artificial Intelligence Research This article is approximately 5400 words long and is recommended for a reading time of over 10 minutes. This article will help you understand Transformers from all aspects through a Q&A format. Source: Zhihu Author: Wang Chen, who asks questions @ Zhihu Why summarize Transformers through eighteen questions? There are two … Read more
Source: https://www.zhihu.com/question/362131975/answer/3058958207 Author: Wang Chen, who asks questions @ Zhihu (Authorized) Editor: Jishi Platform Why summarize Transformers through eighteen questions? There are two reasons: First, Transformer is the fourth major feature extractor after MLP, RNN, and CNN, also known as the fourth foundational model; the recently popular chatGPT is also based on Transformer, highlighting its … Read more
Click I Love Computer Vision to get CVML new technologies faster This article is an interpretation of the commonly used Attention mechanism in papers by 52CV fans, reprinted with the author’s permission. Please do not reprint: https://juejin.im/post/5e57d69b6fb9a07c8a5a1aa2 Paper Title: “Attention Is All You Need” Authors: Ashish Vaswani Google Brain Published in: NIPS 2017 Introduction Remember … Read more
Follow the public account “ML_NLP“ Set as “Starred” for heavy content delivered first! “ This article provides a deep analysis of the Transformer model, including the overall architecture, the background and details of the Attention structure, the meanings of QKV, the essence of Multi-head Attention, FFN, Positional Embedding, and Layer Normalization, as well as everything … Read more
In the past two years, the BERT model has become very popular. Most people know about BERT but do not understand what it specifically is. In short, the emergence of BERT has completely changed the relationship between pre-training to generate word vectors and downstream specific NLP tasks, proposing the concept of training word vectors at … Read more
Originally from New Machine Vision Source: towardsdatascience Author: Raimi Karim Edited by: Xiao Qin [Introduction]The recent rapid advancements in the field of NLP are closely related to architectures based on Transformers. This article guides readers to fully understand the self-attention mechanism and its underlying mathematical principles through diagrams and code, and extends to Transformers. BERT, … Read more
Self-Attention Mechanism The Self-Attention mechanism (Self-Attention)https://so.csdn.net/so/search?q=Self-Attention&spm=1001.2101.3001.7020, as a type of attention mechanism, is also known as intra Attention. It is an important component of the famous Transformer model. It allows the model to allocate weights within the same sequence, thereby focusing on different parts of the sequence to extract features. This mechanism is very effective … Read more
Original Source: Machine Heart Edited by: Big Plate Chicken This article introduces a new research on an approximate attention mechanism, HyperAttention, proposed by institutions such as Yale University and Google Research, which accelerates inference time for ChatGLM2 with a context length of 32k by 50%. Transformers have been successfully applied to various learning tasks in … Read more
Question: I have looked through various materials and read the original papers, which detail how Q, K, and V are obtained through certain operations to produce output results. However, I have not found anywhere that explains where Q, K, and V come from. Isn’t the input of a layer just a tensor? Why are there … Read more
In deep learning, especially in the field of natural language processing, the Attention mechanism has become a very important method. Its core idea is to allocate different weights based on the relevance of each element in the input sequence to the current element, thereby achieving dynamic focus on the input sequence. In the Attention mechanism, … Read more