Summary of Learning Under Temporal Label Noise, by Sujay Nagaraj et al.
Learning under Temporal Label Noise
by Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg, Berk Ustun, Thomas Hartvigsen
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers tackle a previously unexplored challenge in time series classification: temporal label noise. This type of noise occurs when the quality of labels varies over time, potentially improving or worsening at different points. To address this issue, the authors propose methods for training noise-tolerant classifiers that can learn from noisy labels and adapt to changes in label quality. By incorporating a temporal understanding of the noise function into existing classification approaches, the authors demonstrate significant performance improvements on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to predict what will happen next in a time series of data – like stock prices or weather patterns. But some of the labels (or information) are wrong! And those errors change over time. This is called “temporal label noise” and it’s a big problem for machine learning models that try to make predictions based on these time series. The authors of this paper figured out how to build special kinds of models that can handle this type of noise, which helps them work better in the real world. |
Keywords
* Artificial intelligence * Classification * Machine learning * Time series
