Summary of Automated Contrastive Learning Strategy Search For Time Series, by Baoyu Jing et al.
Automated Contrastive Learning Strategy Search for Time Series
by Baoyu Jing, Yansen Wang, Guoxin Sui, Jing Hong, Jingrui He, Yuqing Yang, Dongsheng Li, Kan Ren
First submitted to arxiv on: 19 Mar 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 presents an Automated Machine Learning (AutoML) approach called Automated Contrastive Learning (AutoCL) to automatically learn Contrastive Learning Strategies (CLS) for time series datasets and tasks. AutoCL constructs a massive search space covering various aspects of contrastive learning, including data augmentation, embedding transformation, contrastive pair construction, and contrastive losses. The approach uses reinforcement learning to optimize CLS from performance on validation tasks. Experimental results demonstrate that AutoCL can automatically find suitable CLS for given datasets and tasks, and a transferable Generally Good Strategy (GGS) is composed from the candidate CLS found by AutoCL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops an automated way to learn Contrastive Learning Strategies for time series data. It uses machine learning to find the best strategies without needing people to manually try different approaches. The approach works well on various real-world datasets and can even adapt its strategy to work well on new, unseen datasets. |
Keywords
* Artificial intelligence * Data augmentation * Embedding * Machine learning * Reinforcement learning * Time series
