Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered promptly! From | Zhihu Author | JayLou Link | https://zhuanlan.zhihu.com/p/53682800 Editor | Deep Learning Matters public account This article is for academic sharing only. If there is any infringement, please contact us to delete it. This article summarizes the attention mechanism in natural language … Read more
↑ ClickBlue Text Follow the Jishi Platform Author丨Cool Andy @ Zhihu Source丨https://zhuanlan.zhihu.com/p/125145283 Editor丨Jishi Platform Jishi Guide This article discusses the Attention mechanism in deep learning. It is not intended to review the various frameworks and applications of the Attention mechanism, but rather to introduce four representative and interesting works related to Attention and provide further … Read more
Click the “MLNLP” above to select the “Star” public account. Heavyweight content delivered to you first. From | Zhihu Author | Lucas Address | https://zhuanlan.zhihu.com/p/88376673 Column | Deep Learning and Sentiment Analysis Editor | Machine Learning Algorithms and Natural Language Processing Understanding Attention: The Attention Mechanism and Its PyTorch Implementation Biomimetic Brain Attention Model -> … Read more
Original Author: Adam Kosiorek Annie, Translated from GitHub QbitAI | WeChat Official Account QbitAI The attention mechanism in neural networks has garnered significant attention. In this article, I will attempt to find commonalities and use cases of different mechanisms, explaining the principles and implementations of two types of soft visual attention. What is Attention? In … Read more
LangGraph is an innovative Graph Neural Network (GNN) technology designed to address the complex relationship modeling challenges in Natural Language Processing (NLP) tasks. Traditional NLP models often treat text data as linear sequences, overlooking the intricate relationships between entities within the text. In contrast, LangGraph constructs a graph structure to represent the entities and their … Read more