Summary of Improving Adaptivity Via Over-parameterization in Sequence Models, by Yicheng Li et al.
Improving Adaptivity via Over-Parameterization in Sequence Models
by Yicheng Li, Qian Lin
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In this paper, researchers explore how the order of eigenfunctions in kernel regression affects outcomes. They demonstrate that even with the same set of eigenfunctions, the order can significantly impact results. To capture these effects, they introduce an over-parameterized gradient descent method in sequence models. This approach is designed to investigate the impact of varying eigenfunction orders and shows promise in adapting to signal structure and outperforming vanilla methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how changing the order of eigenfunctions in kernel regression affects outcomes. It’s like looking at a picture from different angles – even if you have the same parts, the perspective changes things. The researchers show that their new method can make better predictions by using more eigenfunctions and adjusting to what they’re trying to capture. |
Keywords
» Artificial intelligence » Gradient descent » Regression
