Read the original document by opening this link in a new tab.
Table of Contents
Intro
The pipeline
Problem-solving approaches
Our problem-solving approach
Can we get the best of both worlds?
Our case study
Reinforcement learning setup
Code time!
Planning
Algorithm to the rescue
Value Iteration
Value Iteration Networks
Moving beyond known world-models
Bridging the gap between the algorithm and its application
Algorithmic bottleneck
Breaking the bottleneck
Breaking the bottleneck with GNNs
Synthetic data
Results
Studying the executor
Studying the algorithmic bottleneck
Conclusions
Deploying-NAR conclusions
Deploying-NAR next steps
Thank you! Questions?
Summary
This document discusses the deployment of Neural Algorithmic Reasoning, focusing on topics like reinforcement learning, value iteration, Value Iteration Networks, and breaking algorithmic bottlenecks. It highlights the combination of classical algorithms with neural networks to enhance real-world solutions. The document also suggests future directions for research in the field of neural algorithmic learning.