Zobrazeno 1 - 10
of 276
pro vyhledávání: '"Huang, Jincai"'
Autor:
Dai, Yang, Ma, Oubo, Zhang, Longfei, Liang, Xingxing, Hu, Shengchao, Wang, Mengzhu, Ji, Shouling, Huang, Jincai, Shen, Li
Transformer-based trajectory optimization methods have demonstrated exceptional performance in offline Reinforcement Learning (offline RL), yet it poses challenges due to substantial parameter size and limited scalability, which is particularly criti
Externí odkaz:
http://arxiv.org/abs/2405.12094
Autor:
Zhu, Zhengqiu, Zhao, Yong, Chen, Bin, Qiu, Sihang, Xu, Kai, Yin, Quanjun, Huang, Jincai, Liu, Zhong, Wang, Fei-Yue
The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs). Therefore, the advance
Externí odkaz:
http://arxiv.org/abs/2402.06654
The Bin Packing Problem (BPP) is a well-established combinatorial optimization (CO) problem. Since it has many applications in our daily life, e.g. logistics and resource allocation, people are seeking efficient bin packing algorithms. On the other h
Externí odkaz:
http://arxiv.org/abs/2312.08103
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14268-14276 (2023)
Traffic congestion event prediction is an important yet challenging task in intelligent transportation systems. Many existing works about traffic prediction integrate various temporal encoders and graph convolution networks (GCNs), called spatio-temp
Externí odkaz:
http://arxiv.org/abs/2311.08635
Autor:
Zhao, Yong, Zhu, Zhengqiu, Chen, Bin, Qiu, Sihang, Huang, Jincai, Lu, Xin, Yang, Weiyi, Ai, Chuan, Huang, Kuihua, He, Cheng, Jin, Yucheng, Liu, Zhong, Wang, Fei-Yue
The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in Artificial systems, Compu
Externí odkaz:
http://arxiv.org/abs/2311.12838
Meta learning is a promising paradigm to enable skill transfer across tasks. Most previous methods employ the empirical risk minimization principle in optimization. However, the resulting worst fast adaptation to a subset of tasks can be catastrophic
Externí odkaz:
http://arxiv.org/abs/2310.00708
Autor:
Zhang, Bingxu, Fan, Changjun, Liu, Shixuan, Huang, Kuihua, Zhao, Xiang, Huang, Jincai, Liu, Zhong
Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works
Externí odkaz:
http://arxiv.org/abs/2308.08235
The concept of GenAI has been developed for decades. Until recently, it has impressed us with substantial breakthroughs in natural language processing and computer vision, actively engaging in industrial scenarios. Noticing the practical challenges,
Externí odkaz:
http://arxiv.org/abs/2308.02561
Autor:
Jin, Guangyin, Liang, Yuxuan, Fang, Yuchen, Shao, Zezhi, Huang, Jincai, Zhang, Junbo, Zheng, Yu
With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which
Externí odkaz:
http://arxiv.org/abs/2303.14483
Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existing works utilize sophisticated temporal modeling approaches to incorpor
Externí odkaz:
http://arxiv.org/abs/2207.10830