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1
Introduction
2
Classical Algorithms
3
Overview
4
Motivation
5
Optimal Path
6
Neural Algorithms
7
Data Efficiency
8
EndtoEnd Pipeline
9
Algorithmic Reasoning
10
General Idea
11
Summary
12
Graph Neural Networks
13
Dynamic Programming
14
Dynamic Programming Example
15
Mathematical Preparation
16
Illustration
17
Aggregation
18
Data Flow Diagram
19
Conclusion
20
Questions
Description:
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.

Graph Neural Networks Are Dynamic Programmers

IEEE Signal Processing Society
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