Grokking - Generalization Beyond Overfitting on Small Algorithmic Datasets

Explore the fascinating phenomenon of "grokking" in neural networks through this in-depth video explanation of a research paper. Delve into how neural networks can suddenly learn patterns in small algorithmic datasets, jumping from random chance to perfect generalization. Examine the emergence of underlying binary operations in learned latent spaces, and investigate factors influencing grokking. Gain insights into the role of smoothness, the importance of simple explanations, and the impact of weight decay on encouraging simplicity in neural network learning. Follow along with a comprehensive outline covering key topics such as double descent, binary operations datasets, and emerging structures in neural network learning.

- Intro & Overview
- The Grokking Phenomenon
- Related: Double Descent
- Binary Operations Datasets
- What quantities influence grokking?
- Learned Emerging Structure
- The role of smoothness
- Simple explanations win
- Why does weight decay encourage simplicity?
- Appendix
- Conclusion & Comments