Hierarchical Attention-based Framework Introduction Hierarchical Text Classification (HTC) refers to a given hierarchical label system (typically a tree structure or directed acyclic graph structure) that predicts the label path of the text (the parent node labels contain the child node labels along the path). Generally, there is at least one label at each level, making … Read more
Click on the above “Beginner’s Guide to Vision“, choose to add “Star” or “Pin“ Important content delivered first This article is reproduced from Zhihu, with the author’s permission. https://zhuanlan.zhihu.com/p/146130215 Previously, I was looking at the self-attention in the DETR paper, and combined with the attention mechanism often mentioned in the lab meetings, I spent time … Read more
Follow us on WeChat “ML_NLP“ Set as “Starred“, heavy content delivered to you first! Reprinted from | High Energy AI This article summarizes the Attention mechanism in Natural Language Processing (NLP) in a Q&A format and provides an in-depth analysis of the Transformer. Table of Contents 1. Analysis of Attention Mechanism 1. Why introduce the … Read more
Click the above “Beginner Learning Vision“, choose to add “Star” or “Top“ Important content delivered first In recent years, the Attention mechanism has made significant breakthroughs in fields such as image processing and natural language processing, proving beneficial for enhancing model performance. The Attention mechanism itself aligns with the perception mechanisms of the human brain … Read more
Attention mechanisms have become the foundational architecture for model design; nowadays, it’s almost embarrassing to release a model without any Attention. Since the release of the attention mechanism, the academic community has been continuously modifying Attention in various innovative ways. The modified Attention can enhance the model’s expressive capability, improve cross-modal abilities and interpretability, as … Read more
Follow the WeChat public account “ML_NLP“ Set it as “Starred“, delivering heavy content directly to you! Author: Jiang Runyu, Harbin Institute of Technology SCIR Introduction In recent years, the most impressive achievement in the field of NLP is undoubtedly the pre-trained models represented by Google’s BERT. They continuously break records (both in task metrics and … Read more
Source: Deephub Imba This article has a total of 2700 words, recommended reading time is 5 minutes. This article will give you an understanding of the overall architecture of the Transformer. For many years, deep learning has been continuously evolving. Deep learning practice emphasizes the use of a large number of parameters to extract useful … Read more
Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered first-hand! Source | Zhihu Address | https://zhuanlan.zhihu.com/p/80986272 Author | Chen Chen Editor | Machine Learning Algorithms and Natural Language Processing Public Account This article is for academic sharing only. If there is any infringement, please contact us to delete the article. The model built … Read more
Author:Fareed Khan Translator: Zhao Jiankai,Proofreader: Zhao Ruxuan The transformer architecture may seem intimidating, and you may have seen various explanations on YouTube or blogs. However, in my blog, I will clarify its principles by providing a comprehensive mathematical example. By doing so, I hope to simplify the understanding of the transformer architecture. Let’s get started! … Read more
Source: Deep Learning Enthusiasts This article is about 8000 words long and is recommended to be read in 16 minutes. This article will detail the Vision Transformer (ViT) explained in "An Image is Worth 16×16 Words". Since the concept of “Attention is All You Need” was introduced in 2017, Transformer models have quickly emerged in … Read more