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Distance Learning Technology Adoption: A Motivation Perspective., , and . J. Comput. Inf. Syst., 42 (2): 38-43 (2002)Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data., , , and . CoRR, (2020)Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning., , , , and . CoRR, (2020)Hysteresis modelling and Composite control of pneumatic muscle actuator based on Prandtl-Ishlinskii Model., , , , and . Int. J. Robotics Autom., (2020)Erratum to: Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments., , , , and . Frontiers Inf. Technol. Electron. Eng., 24 (3): 480 (March 2023)Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning., , , , and . IEEE Trans. Mob. Comput., 22 (7): 4056-4069 (July 2023)Improved RBF network torque control in flexible manipulator actuated by PMAs., , , , , and . Robotica, 37 (2): 264-280 (2019)A Method of Predicting the Deposited Film Thickness in IC Fabrication Based on Stacking Ensemble Learning., , and . CSAI, page 467-474. ACM, (2023)Bipartite consensus for a network of wave equations with time-varying disturbances., , and . Syst. Control. Lett., (2020)Explainable prediction of deposited film thickness in IC fabrication with CatBoost and SHapley Additive exPlanations (SHAP) models., , , and . Appl. Intell., 54 (1): 246-263 (January 2024)