Summary of Perturbation-based Graph Active Learning For Weakly-supervised Belief Representation Learning, by Dachun Sun et al.
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning
by Dachun Sun, Ruijie Wang, Jinning Li, Ruipeng Han, Xinyi Liu, You Lyu, Tarek Abdelzaher
First submitted to arxiv on: 24 Oct 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 paper addresses the problem of optimizing labeling resources for semi-supervised belief representation learning in social networks. The goal is to strategically identify valuable messages worth labeling within a budget, maximizing task performance. Despite progress in unsupervised and semi-supervised methods, labeled social data availability can significantly improve performances. Allocating labeling efforts is crucial when resources are limited. The paper proposes PerbALGraph, an active learning strategy that selects messages for labeling based on automatic estimation, without human guidance. This estimator uses prediction variance under designed graph perturbations, which is model-agnostic and application-independent. Experimental results demonstrate the effectiveness of the proposed strategy for belief representation learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem: how to decide which social media posts are worth labeling when we only have limited resources. Right now, we’re really good at using computers to learn about beliefs and ideologies from social networks, but it would be even better if we had more labeled data to work with. So, the goal is to find a way to choose the most important posts to label, so that we can use our computer models more effectively. To do this, the paper proposes a new approach called PerbALGraph, which uses special math tricks to figure out which posts are most likely to be helpful. The results show that this method really works well for learning about beliefs and ideologies from social networks. |
Keywords
» Artificial intelligence » Active learning » Representation learning » Semi supervised » Unsupervised
