Summary of Cosco: a Sharpness-aware Training Framework For Few-shot Multivariate Time Series Classification, by Jesus Barreda et al.
COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification
by Jesus Barreda, Ashley Gomez, Ruben Puga, Kaixiong Zhou, Li Zhang
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 deep learning-based approach to multivariate time series classification has achieved state-of-the-art performance, but requires large expert-labeled training datasets. To address this issue in few-shot settings, where only a limited number of samples per class are available, the authors propose a new learning framework called COSCO. This framework combines a sharpness-aware minimization (SAM) optimization with a Prototypical loss function to improve the generalization ability of deep neural networks for multivariate time series classification problems. The proposed method outperforms existing baseline methods in few-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to teach computers to recognize patterns in data that changes over time. Right now, this is a big challenge because it often requires a lot of labeled data, which can be hard to get. To fix this problem, the authors created a new system called COSCO. It uses two important ideas: one helps the computer learn more quickly, and the other makes sure the computer doesn’t get confused when trying to classify data. |
Keywords
» Artificial intelligence » Classification » Deep learning » Few shot » Generalization » Loss function » Optimization » Sam » Time series
