Summary of Fast: Factorizable Attention For Speeding Up Transformers, by Armin Gerami et al.
FAST: Factorizable Attention for Speeding up Transformers
by Armin Gerami, Monte Hoover, Pranav S. Dulepet, Ramani Duraiswami
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Numerical Analysis (math.NA)
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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 factorable form of attention in transformers reduces the computational complexity from O(N^2) to O(N), making it efficient in high-dimensional spaces. This approach builds upon the original fast multipole method and improved fast Gauss transform, maintaining the full representation of the attention matrix without compromising on sparsification or all-to-all relationships between tokens. The results demonstrate robust performance and promise for various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to make computer vision systems understand complex patterns more efficiently. That’s what this paper is about! Scientists have found a new method to speed up how computers process information, making it work better in high-dimensional spaces. This means it can handle lots of data at once and learn from many different sources. The results show that this new approach works well and could be used for many applications where machines need to understand patterns. |
Keywords
* Artificial intelligence * Attention
