YoVDO

A Sheaf-based Approach to Graph Neural Networks

Offered By: Applied Algebraic Topology Network via YouTube

Tags

Applied Algebraic Topology Courses Deep Learning Courses Geometric Deep Learning Courses Sheaf Theory Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a sheaf-theoretic perspective on Geometric Deep Learning with a focus on Graph Neural Networks. Delve into the limitations of purely geometric approaches when dealing with non-Euclidean data structures, particularly graphs. Examine how sheaf theory provides a more suitable abstraction for understanding and developing novel Graph Neural Network models. Learn about the intersection of applied algebraic topology and deep learning, gaining insights into advanced techniques for analyzing and processing complex data structures beyond traditional Euclidean spaces.

Syllabus

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


Taught by

Applied Algebraic Topology Network

Related Courses

Making Up Our Minds
Models of Consciousness Conferences via YouTube
Sheaves as Computable and Stable Topological Invariants for Datasets
Applied Algebraic Topology Network via YouTube
Sheaf Based Modeling of Wireless Communications
Applied Algebraic Topology Network via YouTube
Exemplars of Sheaf Theory in TDA
Applied Algebraic Topology Network via YouTube
Modeling Shapes and Fields - A Sheaf Theoretic Perspective
Applied Algebraic Topology Network via YouTube