AlphaTensor: Using Reinforcement Learning for Efficient Matrix Multiplication
DeepMind has recently released AlphaTensor, using an AlphaZero-like approach to treat Matrix Multiplication as a single player game, in order to find the minimum number of multiplication steps needed.
There are a lot of problem-specific modifications to the baseline AlphaZero architecture, most notably axial attention and the 3D tensor grid modelling, which likely helps AlphaTensor learn more effectively. Data augmentation also plays a huge role in giving success cases for AlphaTensor to learn from. In the future, I believe a combination of human guided heuristic and AI-led smart "brute force" search will be the key to solving complicated problems.
Slides can be downloaded at: https://github.com/tanchongmin/TensorFlow-Implementations/tree/main/Paper_Reviews
AlphaTensor Paper: https://www.nature.com/articles/s41586-022-05172-4
AlphaGo Zero Paper: https://discovery.ucl.ac.uk/id/eprint/10045895/1/agz_unformatted_nature.pdf
FBHHRBNRSSSHK-Algorithm Paper: https://arxiv.org/abs/2210.04045
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Timestamps:
0:00 Introduction
7:28 How AlphaGo Zero / AlphaZero works
13:33 AlphaTensor: Applying AlphaZero to Matrix Multiplication
15:13 The Simplified Game (Lightbulbs)
17:45 Formulating Matrix Multiplication as a 3D Tensor
24:21 Strassen's Algorithm
31:08 Main Algorithm using 3D Tensors
33:30 How to play the game
35:15 How to reward the agent
39:21 Acting and Learning Process
41:57 Additional Data Augmentation
49:28 Optimising for Hardware
54:15 Training Time
55:04 Network Architecture
1:00:22 Thoughts on why AlphaTensor works
1:06:13 Questions to Ponder
1:09:10 FBHHRBNRSSSHK-Algorithm
1:11:32 Discussion
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AI and ML enthusiast. Likes to think about the essences behind breakthroughs of AI and explain it in a simple and relatable way. Also, I am an avid game creator.
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