Summary of On Multi-stage Loss Dynamics in Neural Networks: Mechanisms Of Plateau and Descent Stages, by Zheng-an Chen et al.
On Multi-Stage Loss Dynamics in Neural Networks: Mechanisms of Plateau and Descent Stages
by Zheng-An Chen, Tao Luo, GuiHong Wang
First submitted to arxiv on: 26 Oct 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 The paper investigates the multi-stage phenomenon in neural network training loss curves, focusing on the small initialization regime. It identifies three stages: initial plateau, initial descent, and secondary plateau. The authors reveal underlying challenges causing slow training during plateau stages and provide a detailed proof for the initial plateau. They also analyze the dynamics of the initial descent stage and examine factors enabling networks to overcome prolonged secondary plateaus. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how neural networks train and what happens during that process. It finds three main stages: when the network starts to learn, then it gets better, and finally, it levels off again. The researchers figure out why training slows down during these plateau periods. They also show how networks can overcome obstacles and improve their performance. |
Keywords
» Artificial intelligence » Neural network
