Summary of Why Rectified Power Unit Networks Fail and How to Improve It: An Effective Theory Perspective, by Taeyoung Kim et al.
Why Rectified Power Unit Networks Fail and How to Improve It: An Effective Theory Perspective
by Taeyoung Kim, Myungjoo Kang
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Rectified Power Unit (RePU) activation functions, introduced as an alternative to traditional ReLU, offer the advantage of being differentiable in neural network construction. However, empirical observations reveal that stacking deep layers with RePU can lead to critical issues, including exploding or vanishing values and training failure, regardless of hyperparameter initialization. This paper aims to identify the causes of this phenomenon and proposes a new activation function that retains RePU’s benefits while addressing its limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RePU activation functions are a new way to make neural networks work better. They’re different from traditional ReLU because they can be changed mathematically, making them more useful for building deep layers. But when we test these new functions, we see that they have some major problems. No matter how we set the initial settings, our neural networks blow up or become too small, and training fails. This paper tries to figure out why this happens and comes up with a new function that fixes RePU’s flaws. |
Keywords
» Artificial intelligence » Hyperparameter » Neural network » Relu
