Summary of Error Bounds Of Supervised Classification From Information-theoretic Perspective, by Binchuan Qi
Error Bounds of Supervised Classification from Information-Theoretic Perspective
by Binchuan Qi
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 explores the expected risk of using deep neural networks for supervised classification from an information-theoretic perspective. The authors introduce model risk, fitting error, and generalization errors, which are linked to the back-propagated gradient, parameter count, smoothness of the distribution, and sample size. They derive upper bounds on these errors and use the triangle inequality to establish a bound on the expected risk. This bound is applied to explain overparameterization, non-convex optimization, and flat minima in deep learning. The paper also provides empirical verification that confirms a significant positive correlation between the theoretical bounds and practical expected risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well deep neural networks work for classifying things from a certain perspective. It breaks down the mistakes made by these networks into three parts: model risk, fitting error, and generalization errors. The authors then find limits on how big these mistakes can be based on things like how smooth the data is and how many samples you have. They use this to explain why sometimes neural networks work really well even when they’re not perfect, and they test their ideas by looking at real-world data. |
Keywords
» Artificial intelligence » Classification » Deep learning » Generalization » Optimization » Supervised
