Summary of Quantifying Task Priority For Multi-task Optimization, by Wooseong Jeong et al.
Quantifying Task Priority for Multi-Task Optimization
by Wooseong Jeong, Kuk-Jin Yoon
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 method addresses the issue of negative transfer in multi-task learning by introducing a novel concept called task priority. This involves evaluating parameter contributions across different tasks and modifying gradients accordingly. The authors demonstrate their approach, named connection strength-based optimization, through two phases: learning task priority within the network and modifying gradients while upholding this priority. Experimental results show significant improvements in multi-task performance compared to earlier gradient manipulation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, scientists are trying to improve how machines learn multiple things at once. Right now, it’s like trying to teach different skills to one student – some skills might conflict and hinder progress. The researchers came up with a clever way to prioritize which skills get more attention during learning. This helps machines learn all the tasks better and avoids negative effects. They tested their approach and showed it works much better than previous methods. |
Keywords
» Artificial intelligence » Attention » Multi task » Optimization
