Summary of Automating Weak Label Generation For Data Programming with Clinicians in the Loop, by Jean Park et al.
Automating Weak Label Generation for Data Programming with Clinicians in the Loop
by Jean Park, Sydney Pugh, Kaustubh Sridhar, Mengyu Liu, Navish Yarna, Ramneet Kaur, Souradeep Dutta, Elena Bernardis, Oleg Sokolsky, Insup Lee
First submitted to arxiv on: 10 Jul 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 A novel approach is proposed to address the challenge of labeling large datasets, particularly in high-dimensional settings like medical images and time-series data. The solution leverages distance functions to bypass the need for explicit weak labeling functions, which are often difficult to express in these domains. Instead, an algorithm queries a domain expert for labels on a representative subset of samples, inducing a labeling on the full dataset. This approach is shown to improve accuracy and F1 scores by 13-28% and 12-19%, respectively, compared to state-of-the-art methods like Snuba. The proposed method has significant implications for medical applications, where high-quality labeled data is often scarce. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to recognize patterns in medical images or time-series data. This is a big challenge because the computer needs lots of labeled examples to learn from. But what if we could use some rough guesses about what these patterns look like, and then fine-tune them with the help of an expert? That’s basically what this paper proposes. It’s a clever way to label large datasets without needing loads of human-labeled data. The results are impressive, showing big improvements over existing methods in medical applications. |
Keywords
* Artificial intelligence * Time series
