Summary of Se(3)-hyena Operator For Scalable Equivariant Learning, by Artem Moskalev and Mangal Prakash and Rui Liao and Tommaso Mansi
SE(3)-Hyena Operator for Scalable Equivariant Learning
by Artem Moskalev, Mangal Prakash, Rui Liao, Tommaso Mansi
First submitted to arxiv on: 1 Jul 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 A novel approach to modeling global geometric context in high-dimensional data is proposed, targeting applications such as biology, chemistry, or vision. Existing methods struggle with quadratic complexity when processing long sequences, while localized approaches sacrifice global information. The SE(3)-Hyena operator is introduced, a long-convolutional model based on the Hyena operator that captures global geometric context at sub-quadratic complexity and maintains equivariance to rotations and translations. Evaluated on equivariant associative recall and n-body modeling tasks, SE(3)-Hyena outperforms or matches equivariant self-attention models while requiring significantly less memory and computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand a big picture of the world, but it’s too complicated to see everything at once. That’s kind of like what scientists face when they try to analyze huge amounts of data from fields like biology or chemistry. They need a way to focus on specific parts while still understanding how they fit into the bigger picture. Existing methods have limitations, like taking too long to process large datasets or losing important details. A new approach called SE(3)-Hyena tries to solve this problem by using a special type of model that can look at the big picture and focus on specific parts quickly and efficiently. |
Keywords
* Artificial intelligence * Recall * Self attention
