Multi-Scale Compositionality: Identifying the Compositional Structures of Social Dynamics Using Deep Learning
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DOI: 10.1371/journal.pone.0118309
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References listed on IDEAS
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- Keer Yang & Guanqun Zhang & Chuan Bi & Qiang Guan & Hailu Xu & Shuai Xu, 2023. "Improving CNN-base Stock Trading By Considering Data Heterogeneity and Burst," Papers 2303.09407, arXiv.org.
- Yuanyuan Yang & Md Muhie Menul Haque & Dongling Bai & Wei Tang, 2021. "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review," Energies, MDPI, vol. 14(21), pages 1-26, October.
- Aggarwal, Sakshi, 2023. "LSTM based Anomaly Detection in Time Series for United States exports and imports," MPRA Paper 117149, University Library of Munich, Germany.
- Xiaorui Shao & Chang-Soo Kim & Palash Sontakke, 2020. "Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN–LSTM," Energies, MDPI, vol. 13(8), pages 1-22, April.
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