Summary of Exploring Information-theoretic Metrics Associated with Neural Collapse in Supervised Training, by Kun Song et al.
Exploring Information-Theoretic Metrics Associated with Neural Collapse in Supervised Training
by Kun Song, Zhiquan Tan, Bochao Zou, Jiansheng Chen, Huimin Ma, Weiran Huang
First submitted to arxiv on: 25 Sep 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 The paper introduces matrix entropy as an analytical tool for studying supervised learning, investigating the information content of data representations and classification head vectors. It reveals that matrix entropy effectively captures variations in information content and similarity among data samples during neural network training. The authors propose Cross-Model Alignment (CMA) loss to optimize fine-tuning of pretrained models. They also introduce two novel metrics: Matrix Mutual Information Ratio (MIR) and Matrix Entropy Difference Ratio (HDR), which quantify interactions between data representations and classification heads in supervised learning. These metrics are used to analyze the dynamics of standard supervised training, linear mode connectivity, and grokking, a phenomenon where a model exhibits generalization long after achieving training data fit. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses math and computer science to understand how neural networks learn. It’s like trying to figure out what information is important in a big dataset. The authors created new tools to help them do this, like a special kind of math problem that measures the quality of the network’s work. They used these tools to study how neural networks change when they’re learning and what makes some models better than others at recognizing patterns. |
Keywords
» Artificial intelligence » Alignment » Classification » Fine tuning » Generalization » Neural network » Supervised
