The EVEREST model, which adopts an egocentric exploration strategy that enables the model to actively investigate boundary cues and perform self-correction, achieves optimal performance on all metrics in the real world urban socio-semantic dataset, demonstrating the superiority of the model.
Qi-Xiu Li, Zhong-Zhi He, Xiang Zhu et al.· 0 citations
This work introduces Slow-OCast, a transfer-learning based model designed for high-resolution ocean environmental forecasting that incorporates the slow-varying motion characteristics of the ocean and comprises two insightful modules.
Qi-Xiu Li, Xiang Zhu, Xiao-Yong Li et al.· Proceedings of the 32nd ACM...· 0 citations
Token Importance-Aware Policy Optimization is proposed, a novel reinforcement learning strategy that explicitly leverages token-importance awareness and identifies core tokens based on token dependency and reweights a trajectory's advantage according to its overall dependencies.
Qi-Xiu Li, Chen-Long Bao, Xiang Zhu et al.· 0 citations
Regional high-resolution ocean environmental forecasting combines spatial numerical modeling with temporal prediction, and is essential for monitoring the ecological security of specific ocean regions. In recent years, deep learning methods are generally more computationally efficient than traditional numerical models...
Qixiu Li, Xiang Zhu, Xiaoyong Li et al.· Proceedings of the 32nd ACM...· 0 citations
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