Summary of Macformer: Transformer with Random Maclaurin Feature Attention, by Yuhan Guo et al.
Macformer: Transformer with Random Maclaurin Feature Attention
by Yuhan Guo, Lizhong Ding, Ye Yuan, Guoren Wang
First submitted to arxiv on: 21 Aug 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 Macformer architecture employs random Maclaurin features (RMF) to approximate various dot-product kernels, enabling efficient attention computations for long sequences. Inspired by random feature attention (RFA), Macformer combines Random Maclaurin Feature Attention (RMFA) with pre-post Scaling Batch Normalization (ppSBN) to guarantee the accuracy of RMFA and accelerate attention computations. Experiments on the long range arena (LRA) benchmark demonstrate the acceleration and accuracy of Macformer with different dot-product kernels, consistent with theoretical analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Macformer is a new way to make computers understand long sequences of information quickly and accurately. It uses random features to help machines focus on important parts of the data, making it faster than other methods. This architecture has two main parts: one that helps machines understand dot-product kernels and another that makes sure the results are accurate. Researchers tested Macformer with different types of dot-product kernels and found that it works well and is fast. |
Keywords
» Artificial intelligence » Attention » Batch normalization » Dot product
