Главная
Study mode:
on
1
Introduction
2
Definitions
3
Data representations
4
Web document categorization
5
Graph of graphs
6
Encoding and Output Network
7
Learning Process
8
Learning Process Summary
9
Applications
10
Action Recognition
11
Conclusion
Description:
Explore graph neural networks (GNNs) in this 21-minute conference talk from YOW! 2018. Delve into the architecture and applications of GNNs, a variant of deep neural networks designed to model data represented as generic graphs. Learn about graph representation, including graph of graphs (GoGs), and how different data types can be represented using graphs. Discover the architecture of deep graph neural networks and their learning algorithms. Examine practical applications of GoGs and GNNs, such as document classification, web spam detection, and human action recognition in video. Gain insights into how GNNs differ from convolutional neural networks and their potential in advancing artificial intelligence across various domains.

Graph Neural Networks - Algorithm & Applications

GOTO Conferences
Add to list