Summary of Bridging the Gap Between Approximation and Learning Via Optimal Approximation by Relu Mlps Of Maximal Regularity, By Ruiyang Hong et al.
Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
by Ruiyang Hong, Anastasis Kratsios
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Functional Analysis (math.FA); Numerical Analysis (math.NA); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
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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 relationship between approximation and learning theory in deep learning. It highlights the tension between models that need not generalize but are large and expressive, versus those that generalize well but may be too small or constrained to be universal approximators. The authors investigate whether there exists a class of neural networks that balances expressiveness and generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how deep learning works. It talks about big models that don’t need to guess everything correctly, versus smaller models that are good at making predictions but might not be good at anything else. The question is whether there’s a middle ground where models can be both really good and also general enough. |
Keywords
» Artificial intelligence » Deep learning
