Summary of Generalization Error Curves For Analytic Spectral Algorithms Under Power-law Decay, by Yicheng Li et al.
Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay
by Yicheng Li, Weiye Gan, Zuoqiang Shi, Qian Lin
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST)
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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 In this paper, researchers focus on a specific kernel regression method that aims to determine the exact order of generalization error under various conditions. They provide a full characterization of the generalization error curves for kernel gradient descent and other analytic spectral algorithms. This work improves our understanding of the generalization behavior of wide neural networks and has implications for training these networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how a special type of machine learning model works well or poorly when faced with different kinds of data. The researchers want to know exactly why this happens, so they provide a detailed map of what makes their method good at predicting results. This helps us understand how to make neural networks work better. |
Keywords
* Artificial intelligence * Generalization * Gradient descent * Machine learning * Regression
