Summary of Low-rank Approximation Of Structural Redundancy For Self-supervised Learning, by Kang Du and Yu Xiang
Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning
by Kang Du, Yu Xiang
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 investigates the data-generating mechanism behind self-supervised learning (SSL) and its effectiveness. The authors provide a sufficient and necessary condition for perfect linear approximation, revealing two components: one that preserves label classes of Y and another redundant component. To approximate this redundant component, they propose low-rank factorization and measure its quality using a new quantity epsilon_s, which is parameterized by the rank of the factorization. The authors analyze excess risk under both linear regression and ridge regression settings, incorporating epsilon_s into their analysis. They design three stylized experiments to compare SSL with supervised learning under different settings, supporting their theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how self-supervised learning (SSL) works and why it’s effective. The researchers found a special condition that makes perfect linear approximation possible. This condition shows two parts: one that keeps track of Y’s label classes and another part that can be ignored. To make this second part easier to understand, they suggest breaking it down into smaller pieces. They also came up with a new way to measure how well these small pieces are working together. The authors analyzed how SSL compares to traditional supervised learning in different situations, showing that SSL is a good approach. |
Keywords
* Artificial intelligence * Linear regression * Regression * Self supervised * Supervised
