Summary of Information-theoretic Progress Measures Reveal Grokking Is An Emergent Phase Transition, by Kenzo Clauw et al.
Information-Theoretic Progress Measures reveal Grokking is an Emergent Phase Transition
by Kenzo Clauw, Sebastiano Stramaglia, Daniele Marinazzo
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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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 The paper investigates the phenomenon of neural networks suddenly generalizing after delayed memorization, known as “grokking.” By analyzing the collective behavior and shared properties between neurons during training using higher-order mutual information, researchers identify distinct phases before grokking occurs. They attribute this phase transition to synergistic interactions between neurons and demonstrate that weight decay and weight initialization can enhance it. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how neural networks suddenly start working well after a period of getting better at recognizing patterns. Researchers want to understand why this happens, so they studied the behavior of individual neurons during training. They found that there are different stages before this “grokking” point is reached and that it’s caused by how neurons work together. The study shows that controlling certain aspects of neural networks can help them learn faster. |
