Summary of Towards Better Multi-head Attention Via Channel-wise Sample Permutation, by Shen Yuan et al.
Towards Better Multi-head Attention via Channel-wise Sample Permutation
by Shen Yuan, Hongteng Xu
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 channel-wise sample permutation (CSP) operator is a novel and simple mechanism that achieves a new structured multi-head attention (MHA) with fewer parameters and lower complexity. CSP is equivalent to implementing cross-channel attention maps as permutation matrices, which reduces the risk of rank collapse when representing data. By replacing the MHA in representative models with CSP, experiments show that the CSP-based models achieve comparable or better performance with fewer parameters and lower computational costs than classic Transformer and its state-of-the-art variants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to do something important in deep learning called multi-head attention. They make it simpler and faster by using an operator called channel-wise sample permutation, or CSP for short. This lets them use the same idea with fewer calculations and less computer power needed. They test this with some popular models and show that it works just as well or even better than before. |
Keywords
» Artificial intelligence » Attention » Deep learning » Multi head attention » Transformer
