Summary of Towards Principled Task Grouping For Multi-task Learning, by Chenguang Wang et al.
Towards Principled Task Grouping for Multi-Task Learning
by Chenguang Wang, Xuanhao Pan, Tianshu Yu
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed approach to task grouping in Multitask Learning (MTL) improves upon existing methods by addressing theoretical and practical limitations. The novel method offers a more grounded approach that doesn’t rely on restrictive assumptions for transfer gains. A flexible mathematical programming formulation is also introduced, accommodating various resource constraints. Experimental results across computer vision, combinatorial optimization, and time series tasks demonstrate the superiority of this method over extensive baselines, validating its effectiveness in MTL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows a new way to group tasks together in Multitask Learning (MTL). It makes the current methods better by fixing some problems they have. The new approach is more realistic because it doesn’t assume certain things need to happen for learning to transfer between tasks. A flexible formula is also introduced, which can be used with different rules for sharing resources. This means the method can be applied in many different areas. The results from testing the method show that it works better than other methods do. |
Keywords
* Artificial intelligence * Optimization * Time series
