Originally from Python Artificial Intelligence Frontier Graph Neural Networks are widely used in recommendation systems, knowledge graphs, and traffic road analysis due to their advantages in processing non-Euclidean space data and complex features. However, when the graph data volume increases, problems arise: computation becomes extremely slow, memory cannot hold it, and communication in distributed systems … Read more
Follow the public account “ML_NLP” Set as “starred” to receive heavy content promptly! Author丨Electric Light Phantom Alchemy @ Zhihu Source丨https://zhuanlan.zhihu.com/p/362366192 Editor丨Machine Learning Algorithms and Natural Language Processing Attention has become a hot topic in the entire AI field, whether in machine vision or natural language processing, it is inseparable from Attention, transformer, or BERT. Below, … Read more
This article is about 4500 words long and is recommended for a reading time of over 10 minutes. This article introduces Graphormer, a graph representation learning method based on the standard Transformer architecture. 1 Introduction The Transformer architecture has shown excellent performance in fields such as natural language processing and computer vision, but it performs … Read more
Source: Algorithm Advancement This article is approximately 4500 words long and is recommended for a 9-minute read. This article introduces the Graph Transformer, a novel and powerful neural network model capable of effectively encoding and processing graph-structured data. Graph neural networks (GNNs) and Transformers represent a recent advancement in machine learning, providing a new type … Read more
Click the above“Beginner’s Guide to Vision” to choose star mark or pin. Important content delivered promptly This article is reproduced from:Machine Heart | Contributors: Yiming, Du Wei, Jamin Author:Chaitanya Joshi What is the relationship between Transformers and GNNs? It may not be obvious at first. However, through this article, you will view the architecture of … Read more
Graph neural networks (GNNs) have gained widespread attention and are applied in scenarios such as recommendation systems, knowledge graphs, and traffic analysis due to their advantages in handling non-Euclidean space data and complex features. The irregularity of large-scale graph structures, the complexity of node features, and the dependency of training samples put immense pressure on … Read more
Resource Sharing Edited by: Rabbit Unknowingly, 2019 is about to pass Time flies, and you have accompanied us for another year This year We have compiled 33 papers and 165 news updates Prepared 46 paper interpretations Shared 38 pieces of technical content Held 6 sessions of AI Talking And organized 1 offline salon called “Graph … Read more
This article will cover the essence of GNN、GNNprinciples、GNNapplications in three aspects, allowing you to understand Graph Neural Networks (GNN) in one article. Graph Neural Network (GNN) 1. The Essence of GNNEssence of GNN The definition of GNN:GNN, or Graph Neural Network, is a deep learning model based on graph structures, specifically designed to handle graph … Read more
How to Obtain Resources 1. Follow the public account below, and click【Like】 and 【View】 2. Click 【Get Course】 to obtain this material. Resources are from Baidu Cloud Disk:《Introduction and Practice of LangGraph Based on Large Model Agent》 Introduction and Practice of LangGraph Based on Large Model Agent In the field of artificial intelligence, with the … Read more
1 Introduction Deep neural networks are composed of neurons organized into layers and interconnected, capturing their architecture through computation graphs, where neurons are represented as nodes and directed edges connect different layers of neurons. The performance of neural networks depends on their architecture, but there is currently a lack of systematic understanding of the relationship … Read more