Summary of Attention Is a Smoothed Cubic Spline, by Zehua Lai et al.
Attention is a smoothed cubic spline
by Zehua Lai, Lek-Heng Lim, Yucong Liu
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); 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 paper reveals a surprising connection between the attention module in transformers and cubic splines from classical approximation theory. Specifically, it shows that with ReLU-activation, various types of attention modules, including masked attention and encoder-decoder attention, are all cubic splines. Since transformer components are composed of these attention modules and feed-forward neural networks (linear splines), the entire transformer architecture can be viewed as a combination of cubic or higher-order splines. This insight sheds light on the nature of transformers by casting them in terms of well-understood mathematical objects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that the attention module in transformers is actually a type of mathematical function called a spline. It explains how different types of attention, like masked attention and encoder-decoder attention, are all related to these splines. This helps us understand how transformers work by seeing them as combinations of simple mathematical functions. |
Keywords
» Artificial intelligence » Attention » Encoder decoder » Relu » Transformer
