Summary of A Large Dimensional Analysis Of Multi-task Semi-supervised Learning, by Victor Leger et al.
A Large Dimensional Analysis of Multi-task Semi-Supervised Learning
by Victor Leger, Romain Couillet
First submitted to arxiv on: 21 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 presents a large-scale study of a versatile classification model that combines multi-task and semi-supervised learning with uncertain labeling. The model’s asymptotics are characterized using tools from random matrix theory, enabling predictions of performance and counter-intuitive guidance on usage. The model’s simplicity belies its power in providing good performance guarantees and insights into behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a new way to help computers learn by combining different types of learning together. They study how well this method works when some of the labels are uncertain, and use special math tools to understand what makes it work or not. This helps us predict how well the method will do and even gives us tips on how to make it better. |
Keywords
* Artificial intelligence * Classification * Multi task * Semi supervised
