Summary of Does Equivariance Matter at Scale?, by Johann Brehmer et al.
Does equivariance matter at scale?
by Johann Brehmer, Sönke Behrends, Pim de Haan, Taco Cohen
First submitted to arxiv on: 30 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 whether designing neural architectures according to problem structure and symmetries is beneficial or if learning them from data is more efficient. The authors study the scaling of equivariant and non-equivariant networks with compute and training samples, focusing on rigid-body interactions and transformer architectures. They find that equivariance improves data efficiency but can be matched by non-equivariant models with sufficient epochs. Scaling with compute follows a power law, with equivariant models outperforming non-equivariant ones at each tested budget. The optimal allocation of a compute budget differs between equivariant and non-equivariant models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at whether you should design special neural networks for specific problems or just learn the right way to solve them from lots of data. They test different types of neural networks, called equivariant and non-equivariant, to see how well they work when given more computer power or training data. They found that making special networks can help use less data, but regular networks can catch up if you give it enough time. Also, the best way to use your computer power depends on what kind of network you’re using. |
Keywords
» Artificial intelligence » Transformer
