Summary of The Complexity Dynamics Of Grokking, by Branton Demoss et al.
The Complexity Dynamics of Grokking
by Branton DeMoss, Silvia Sapora, Jakob Foerster, Nick Hawes, Ingmar Posner
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates how neural networks generalize solutions long after over-fitting training data. The study focuses on “grokking,” where networks suddenly transition from memorization to generalizing. To understand this phenomenon, the researchers introduce a new measure of intrinsic complexity based on Kolmogorov complexity theory. They track this metric throughout network training and find a consistent pattern: a rise and fall in complexity corresponding to memorization followed by generalization. The paper also develops a principled approach to lossy compression of neural networks using rate-distortion theory and the minimum description length principle. Additionally, it proposes a new regularization method that encourages low-rank representations by penalizing spectral entropy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how neural networks learn and improve over time. It’s like trying to figure out why you suddenly got better at playing a game after practicing for hours. The scientists came up with a new way to measure the “complexity” of these networks, which is like counting how many different things they can do. They found that as the network learns, its complexity goes up and then down in a specific pattern. This helps us understand why networks go from just memorizing data to actually learning and generalizing. The paper also shows how we can compress neural networks to make them more efficient, which is important for using them on devices with limited power. |
Keywords
» Artificial intelligence » Generalization » Regularization
