Summary of Scalable Multitask Learning Using Gradient-based Estimation Of Task Affinity, by Dongyue Li et al.
Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity
by Dongyue Li, Aneesh Sharma, Hongyang R. Zhang
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI); 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 The proposed paper introduces a novel algorithm called Grad-TAG, designed to estimate task affinities in multitask learning settings without the need for repetitive training on diverse task combinations. This approach is particularly relevant in graph neural networks and language model fine-tuning applications, where tasks may interfere with each other. The authors focus on modeling pairwise task affinity and higher-order affinity among subsets of tasks, enabling more effective modeling of relationships between tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new algorithm called Grad-TAG helps figure out how different tasks are related without needing to train models many times. This is useful when trying to learn multiple things at once, like in graph neural networks or language models. The goal is to understand how individual tasks connect with each other. |
Keywords
» Artificial intelligence » Fine tuning » Language model
