According to @godofprompt, graph databases offer superior efficiency for dynamic updates in AI-powered knowledge bases compared to traditional vector search methods. When using vector search, any ...

Abstract: Dynamic graph representation learning aims to generate low-dimensional latent vector representations of graphs or nodes at various time points from evolving graph datas, which are then used ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Getting a handle on LeetCode can feel like a big task, especially when you’re starting out. But with the right approach and tools, it becomes much more manageable. Python, with its clear syntax and ...

Abstract: This paper focuses on representation learning for dynamic graphs with temporal interactions. A fundamental issue is that both the graph structure and the nodes own their own dynamics, and ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Large Language Models (LLMs) have revolutionized many areas of natural language processing, but they still face critical limitations when dealing with up-to-date facts, domain-specific information, or ...

Introduction: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, ...

He works in mysterious ways.

Introduction: Voxel hierarchy on dynamic brain graphs is produced by k-core percolation on functional dynamic amplitude correlation of resting-state fMRI. Methods: Directed graphs and their ...