Summary of Remove Symmetries to Control Model Expressivity and Improve Optimization, by Liu Ziyin et al.
Remove Symmetries to Control Model Expressivity and Improve Optimization
by Liu Ziyin, Yizhou Xu, Isaac Chuang
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 research paper explores how symmetries in loss functions can cause deep learning models to become trapped in low-capacity states, hindering training and inference processes. The authors identify two mechanisms by which symmetries lead to reduced capacities and ignored features during training and inference. To address this issue, the researchers propose a simple algorithm, syre, that removes symmetry-induced low-capacity states in neural networks, allowing for improved optimization and performance. This model-agnostic method does not require knowledge of the symmetry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models can get stuck in a low-capacity state when symmetries are present in the loss function. This can happen during training or inference, and it’s a major obstacle to applying deep learning technology. The authors of this paper explain how symmetries cause this problem and propose a solution called syre. Syre is a simple algorithm that removes these symmetry-induced low-capacity states, leading to better performance. |
Keywords
» Artificial intelligence » Deep learning » Inference » Loss function » Optimization
