Cristian Bodnar (11/7/23): A Sheaf-based Approach to Graph Neural Networks скачать в хорошем качестве

Cristian Bodnar (11/7/23): A Sheaf-based Approach to Graph Neural Networks

The multitude of applications where data is attached to spaces with non-Euclidean structure has driven the rise of the field of Geometric Deep Learning (GDL). Nonetheless, from many points of view, geometry does not always provide the right level of abstraction to study all the spaces that commonly emerge in such settings. For instance, graphs, by far the most prevalent type of space in GDL, do not even have a geometrical structure in the strict sense. In this talk, I will explore how we can take a sheaf-theoretic perspective of the field with a focus on understanding and developing new Graph Neural Network models.