Multivariate Time-Series Representation Learning via Hierarchical Correlation Pooling Boosted Graph Neural Network-Reference-Cited by-同舟云学术

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Multivariate Time-Series Representation Learning via Hierarchical Correlation Pooling Boosted Graph Neural Network

Published:2024-01 Issue:1 Volume:5 Page:321-333
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Wang Yucheng¹,Wu Min¹^ORCID,Li Xiaoli²^ORCID,Xie Lihua³^ORCID,Chen Zhenghua²^ORCID

Affiliation:

1. Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore

2. Institute for Infocomm Research and the Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore

3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

Funder

Agency for Science, Technology and Research under its Career Development Award

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Artificial Intelligence

Link

http://xplorestaging.ieee.org/ielx7/9078688/10384607/10036065.pdf?arnumber=10036065

Reference48 articles.

1. A Deep Adversarial Learning Prognostics Model for Remaining Useful Life Prediction of Rolling Bearing

2. KDnet-RUL: A Knowledge Distillation Framework to Compress Deep Neural Networks for Machine Remaining Useful Life Prediction

3. Graph Convolutional Neural Network for Human Action Recognition: A Comprehensive Survey

4. Time-Series Representation Learning via Temporal and Contextual Contrasting

5. A Model-Based Method for Remaining Useful Life Prediction of Machinery

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1. Early MTS Forecasting for Dynamic Stock Prediction: A Double Q-Learning Ensemble Approach;IEEE Access;2024

2. Graph Neural Network-Based EEG Classification: A Survey;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024

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