Summary of Provable Tempered Overfitting Of Minimal Nets and Typical Nets, by Itamar Harel et al.
Provable Tempered Overfitting of Minimal Nets and Typical Nets
by Itamar Harel, William M. Hoza, Gal Vardi, Itay Evron, Nathan Srebro, Daniel Soudry
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 behavior of fully connected neural networks with binary weights when trained to perfectly classify noisy data. The authors consider two approaches: using the smallest possible network and a random interpolating network. They prove that overfitting is tempered in both cases, relying on a new bound for threshold circuits consistent with partial functions. This work provides the first theoretical results on benign or tempered overfitting for deep neural networks, without requiring high or low input dimensions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study examines how well-performing neural networks can avoid becoming too good at fitting noisy training data. By looking at two different approaches to building these networks, researchers show that even small networks and randomly constructed networks can still learn from the data without overfitting. This is important because it means we can build simpler models that work just as well, which is useful in many real-world applications. |
Keywords
» Artificial intelligence » Overfitting
