Click on the above“Beginner’s Guide to Vision”, select to add “Bookmark” or “Pin” Heavyweight content delivered at the first moment This article is adapted from: Deep Learning Matters Background The dynamic graph framework of PyTorch is primarily implemented in the code under torch/csrc/autograd. This directory defines three main base classes: Variable, Function, Engine, which together … Read more
Author: Deng Yang This article is approximately 6300 words long and is recommended for a 10-minute read. This article briefly introduces the working principles of GAT based on the order discussed in the paper by Velickovic et al. (2017). When numbers are intangible, intuition is sparse; when forms are few, it is hard to delve … Read more
1. Why Introduce LightRAG? Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, allowing LLMs to generate more accurate and contextually relevant responses, significantly improving utility in practical applications. • By adapting to domain-specific knowledge, RAG systems ensure that the information provided is not only relevant but also meets user needs. … 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
Technical Column Author: Liu Zhongyu Edited by Luobotu Today, I want to share with you about Graph Convolutional Networks. With the development of artificial intelligence, many people have heard of concepts like machine learning, deep learning, and convolutional neural networks. However, Graph Convolutional Networks are not often mentioned. So, what are Graph Convolutional Networks? Simply … Read more
How to Define the Input and Output Schema of a Graph By default, the StateGraph operates using a single schema, and all nodes should communicate using that schema. However, different input and output schemas can also be defined for the graph. When different schemas are specified, the internal schema will still be used for communication … Read more
A very impressive RAG system introduction. For more resources, please visit: I’m currently discussing interesting topics with friends in the “Large Model Pit Avoidance Guide,” would you like to join? [Code Repository] GitHub
The development of Knowledge Graphs (KG) is closely linked to the advancements in Artificial Intelligence (AI) agents. Starting from their static origins, knowledge graphs have evolved to include dynamic, temporal, and event-driven paradigms, each unlocking new capabilities for AI systems. This article explores their evolution and how Large Language Models (LLM) integrate into these advancements. … 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