Summary of The Effect Of Leaky Relus on the Training and Generalization Of Overparameterized Networks, by Yinglong Guo et al.
The effect of Leaky ReLUs on the training and generalization of overparameterized networks
by Yinglong Guo, Shaohan Li, Gilad Lerman
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 investigates the training and generalization errors of overparameterized neural networks (NNs) with a wide class of leaky rectified linear unit (ReLU) functions. The authors derive upper bounds for both the convergence rate of the training error and the generalization error, and study how these bounds depend on the Leaky ReLU parameter α. They find that α = -1, corresponding to the absolute value activation function, is optimal for both the training and generalization error bounds in certain settings. Empirical experiments support these theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how leaky ReLU functions affect neural networks. Scientists tried to understand how well-trained models do when tested with new data. They found that the Leaky ReLU parameter matters a lot! For some values, it makes training and testing errors lower. The researchers also did experiments to see if their ideas work in real-life situations. |
Keywords
* Artificial intelligence * Generalization * Relu
