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
Spatio-temporal data is the foundation of many intelligent applications, revealing the causal relationships between measurements at a specific location and historical data from the same or other locations. In this context, Adaptive Spatio-Temporal Graph Neural Networks (ASTGNNs) have emerged as a powerful tool for modeling these dependencies, particularly through data-driven approaches rather than predefined spatial … Read more