Geometry is regarded as one of the promising avenues for advancing machine learning and deep learning in general. Data in biology, physics, computer graphics, social networks are usually not vectors in Euclidean space but objects on a manifold. The study of non-Euclidean data brings many challenges: the data is not only high-dimensional but also has an intricate structure of internal relation. The data geometry study has been a central topic in fields such as data science, topological data analysis, and more recently, graph neural network. The latter is an emerging field that explores how deep learning technology and theory can be generalized to non-Euclidean data. It provides a useful tool for AI drug discovery and 3D object shape analysis in self-driving due to its outstanding performance and a relatively simple network architecture. The study of graph neural network has become a global trend with people realizing its potential. This talk will introduce graph framelet systems and how framelet based signal processing enhances graph neural networks.

10-28海报.pdf