Summary of Activedp: Bridging Active Learning and Data Programming, by Naiqing Guan et al.
ActiveDP: Bridging Active Learning and Data Programming
by Naiqing Guan, Nick Koudas
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
GrooveSquid.com Paper Summaries
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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 proposes an interactive framework called ActiveDP, which combines the strengths of active learning and data programming to efficiently generate accurate labels for large datasets. By leveraging the best of both paradigms, ActiveDP aims to bridge the gap between noisy labels produced by data programming and accurate labels acquired through active learning. The authors experimentally demonstrate that ActiveDP outperforms previous approaches in terms of label accuracy and coverage, making it a promising solution for addressing the challenges of large-scale dataset labelling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to make big datasets easier to use by combining two ideas: active learning and data programming. Right now, we have to manually label lots of data, which takes forever and is very expensive. Data programming makes this process faster, but it gives us wrong labels sometimes. Active learning helps us get accurate labels for a few samples, but not many. The new method, called ActiveDP, brings these two ideas together to create high-quality labels efficiently. It works really well, even when we have limited time and resources. |
Keywords
* Artificial intelligence * Active learning
