Summary of Levattention: Time, Space, and Streaming Efficient Algorithm For Heavy Attentions, by Ravindran Kannan et al.
LevAttention: Time, Space, and Streaming Efficient Algorithm for Heavy Attentions
by Ravindran Kannan, Chiranjib Bhattacharyya, Praneeth Kacham, David P. Woodruff
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 addresses a central problem in transformer models, which involves computing attention scores efficiently. It proposes an algorithm to find all “large attention scores” in time linear with the size of the input context. The approach uses recently developed tools from randomized numerical linear algebra and is applicable to various functions, including those used in recent transformer models. The paper also introduces the LevAttention mechanism, which leverages leverage scores to identify a universal set of keys that can be used for attention computation. Experimental results demonstrate the effectiveness of this scheme for vision transformers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves an important problem in transformer models by creating an efficient way to compute attention scores. It does this by using special tools from math and computer science. This helps make transformer models faster and more useful for big tasks like image recognition. The new method is also really good at picking the most important parts of the data, which makes it very useful. |
Keywords
* Artificial intelligence * Attention * Transformer
