Summary of A Primal-dual Framework For Transformers and Neural Networks, by Tan M. Nguyen et al.
A Primal-Dual Framework for Transformers and Neural Networks
by Tan M. Nguyen, Tam Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 This paper provides a principled framework for constructing attention layers in transformers by showing that self-attention corresponds to the support vector expansion derived from a support vector regression problem. Using this framework, the authors derive popular attention layers used in practice and propose two new attentions: Batch Normalized Attention (Attention-BN) and Attention with Scaled Head (Attention-SH). These new attentions reduce head redundancy, increase model accuracy, and improve efficiency in image and time-series classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are super good at doing stuff with words and pictures. They use something called self-attention to make it all work. But people usually just figure out how to do this attention thing by trying different things until they get the right result. This paper shows that there’s a better way to do it, based on math and science-y things like support vector regression. It even comes up with new ways to do attention that are better than what people are doing now. This is cool because it can help make computers better at recognizing pictures and patterns in data. |
Keywords
» Artificial intelligence » Attention » Classification » Regression » Self attention » Time series
