Explore the intersection of graph neural networks and dynamic programming in this IEEE Signal Processing Society webinar. Delve into classical algorithms, optimal path finding, and neural approaches as Petar Veličković from Deepmind guides you through data efficiency, end-to-end pipelines, and algorithmic reasoning. Examine mathematical preparations, data flow diagrams, and practical illustrations to understand how graph neural networks function as dynamic programmers. Gain insights into this cutting-edge topic and its applications in data science and signal processing.
Overview
Syllabus
Introduction
Classical Algorithms
Overview
Motivation
Optimal Path
Neural Algorithms
Data Efficiency
EndtoEnd Pipeline
Algorithmic Reasoning
General Idea
Summary
Graph Neural Networks
Dynamic Programming
Dynamic Programming Example
Mathematical Preparation
Illustration
Aggregation
Data Flow Diagram
Conclusion
Questions
Taught by
IEEE Signal Processing Society
Related Courses
-
Robustness of Graph Neural Networks
-
Neural Diffusion PDEs, Differential Geometry, and Graph Neural Networks
-
Theory of Graph Neural Networks: Representation and Learning
-
Graph Neural Networks for Multi-Agent Learning
-
Neural Nets for NLP 2017 - Parsing With Dynamic Programs
-
Understanding Graph Neural Networks - Part 1
