Successive model-agnostic meta-learning for few-shot fault time series prognosis

Author:

Su HaiORCID,Hu Jiajun,Yu SongsenORCID,Liu JuhuaORCID,Qin Xiangyang

Publisher

Elsevier BV

Reference48 articles.

1. Jiehui Xu, et al., Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy, in: International Conference on Learning Representations, 2021, pp. 1–20.

2. Sebastian Pineda Arango, et al., Multimodal Meta-Learning for Time Series Regression, in: International Workshop on Advanced Analytics and Learning on Temporal Data, 2021, pp. 123–128.

3. Jyoti Narwariya, et al., Meta-learning for few-shot time series classification, in: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, 2020, pp. 28–36.

4. Few-shot RUL estimation based on model-agnostic meta-learning;Mo;J. Intell. Manuf.,2022

5. Chelsea Finn, Pieter Abbeel, Sergey Levine, Model-agnostic meta-learning for fast adaptation of deep networks, in: International Conference on Machine Learning, 2017.

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