Summary of Learning Infinitesimal Generators Of Continuous Symmetries From Data, by Gyeonghoon Ko et al.
Learning Infinitesimal Generators of Continuous Symmetries from Data
by Gyeonghoon Ko, Hyunsu Kim, Juho Lee
First submitted to arxiv on: 29 Oct 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 proposed algorithm leverages symmetry inherent in data to improve sample efficiency and model generalization, extending beyond traditional Lie group-based methods. By parameterizing transformations using one-parameter groups and infinitesimal generators, the approach encompasses linear, affine, and nonlinear symmetries. A novel validity score is introduced for examining transformed data’s task relevance, enabling effective searches. The algorithm is applied to image data and partial differential equations, demonstrating its advantages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to use symmetry in data to make machine learning more efficient and accurate. It introduces an algorithm that can find symmetries not just in simple transformations like rotations or flips, but also in more complex ones. This is achieved by using special mathematical objects called one-parameter groups and infinitesimal generators. The algorithm also includes a way to check if the transformed data is still suitable for the task at hand, which helps it find the best symmetries. The method is tested on image data and partial differential equations, showing its effectiveness. |
Keywords
» Artificial intelligence » Generalization » Machine learning
