Summary of Symmetry Discovery For Different Data Types, by Lexiang Hu et al.
Symmetry Discovery for Different Data Types
by Lexiang Hu, Yikang Li, Zhouchen Lin
First submitted to arxiv on: 13 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 proposed LieSD method enables the discovery of symmetries in neural networks, which can lead to better generalization performance. By approximating input-output mappings, LieSD characterizes equivariance and invariance using Lie algebra and solves the Lie algebra space through inputs, outputs, and gradients. This approach is applicable to multi-channel and tensor data. The method’s performance is validated on tasks such as two-body problem prediction, moment of inertia matrix prediction, and top quark tagging. Compared to baselines, LieSD can accurately determine the number of Lie algebra bases without expensive group sampling. Additionally, LieSD performs well on non-uniform datasets, whereas GAN-based methods fail. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LieSD is a new method that helps neural networks discover symmetries in data. This makes them work better and generalize more accurately. The method uses special mathematical structures called Lie algebras to find these symmetries. It can handle different types of data, like images with multiple channels or 3D data. The researchers tested LieSD on some challenging tasks and showed that it outperforms other methods. |
Keywords
» Artificial intelligence » Gan » Generalization
