Summary of Universal Consistency Of Wide and Deep Relu Neural Networks and Minimax Optimal Convergence Rates For Kolmogorov-donoho Optimal Function Classes, by Hyunouk Ko and Xiaoming Huo
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
by Hyunouk Ko, Xiaoming Huo
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper proves the universal consistency of wide and deep ReLU neural network classifiers trained on the logistic loss, providing sufficient conditions for minimax optimal rates of convergence in various probability measures. The framework encompasses general settings, unlike most previous works that assume explicit smoothness on regression functions. The proposed neural networks are either interpolating classifiers or 0-1 loss minimizers, exhibiting benign overfitting behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers prove that certain types of artificial intelligence models are reliable and consistent in their predictions. They also find conditions where these models can perform optimally. This is important because it helps us understand how these models work in different situations. The results apply to many known cases, but most importantly, they don’t require specific assumptions about the data. |
Keywords
* Artificial intelligence * Neural network * Overfitting * Probability * Regression * Relu
