Summary of Relaxed Equivariance Via Multitask Learning, by Ahmed A. Elhag et al.
Relaxed Equivariance via Multitask Learning
by Ahmed A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, Michael Bronstein
First submitted to arxiv on: 23 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 REMUL training procedure approximates equivariance with multitask learning, enabling unconstrained models to learn approximate symmetries by minimizing an additional simple equivariance loss. This method achieves competitive performance compared to equivariant baselines while being 10x faster at inference and 2.5x at training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Incorporating equivariance into deep learning architectures has been successful in various applications, such as chemistry and dynamical systems. The goal is to take advantage of data symmetry, which is crucial for effectively modeling geometric graphs and molecules. However, equivariant models often pose challenges due to their high computational complexity. |
Keywords
* Artificial intelligence * Deep learning * Inference
