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pro vyhledávání: '"Zhang, LingYu"'
Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling temporal and spatial correlations. However, rapid urbanization in recent years has led to dynamic shifts in
Externí odkaz:
http://arxiv.org/abs/2411.11448
The recent rapid advancement of machine learning has been driven by increasingly powerful models with the growing availability of training data and computational resources. However, real-time decision-making tasks with limited time and sparse learnin
Externí odkaz:
http://arxiv.org/abs/2410.15181
Spatiotemporal neural networks have shown great promise in urban scenarios by effectively capturing temporal and spatial correlations. However, urban environments are constantly evolving, and current model evaluations are often limited to traffic sce
Externí odkaz:
http://arxiv.org/abs/2410.04740
Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture complex nonlinear patterns in spatiotemporal (ST) data, has emerged as a pow
Externí odkaz:
http://arxiv.org/abs/2410.00385
Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have demonstrated promising potential for traffic forecasting by effectively capturing both temporal and spatial correlations. The generalization ability of spatio
Externí odkaz:
http://arxiv.org/abs/2410.00373
Learning collaborative behaviors is essential for multi-agent systems. Traditionally, multi-agent reinforcement learning solves this implicitly through a joint reward and centralized observations, assuming collaborative behavior will emerge. Other st
Externí odkaz:
http://arxiv.org/abs/2409.19831
With the increasing deployment of artificial intelligence (AI) technologies, the potential of humans working with AI agents has been growing at a great speed. Human-AI teaming is an important paradigm for studying various aspects when humans and AI a
Externí odkaz:
http://arxiv.org/abs/2408.00170
Autor:
Huang, Kung-Hsiang, Zhou, Mingyang, Chan, Hou Pong, Fung, Yi R., Wang, Zhenhailong, Zhang, Lingyu, Chang, Shih-Fu, Ji, Heng
Recent advancements in large vision-language models (LVLMs) have led to significant progress in generating natural language descriptions for visual content and thus enhancing various applications. One issue with these powerful models is that they som
Externí odkaz:
http://arxiv.org/abs/2312.10160
Open World Compositional Zero-Shot Learning (OW-CZSL) is known to be an extremely challenging task, which aims to recognize unseen compositions formed from seen attributes and objects without any prior assumption of the output space. In order to achi
Externí odkaz:
http://arxiv.org/abs/2312.02191
Publikováno v:
口腔疾病防治, Vol 32, Iss 10, Pp 772-779 (2024)
Objective To investigate the clinical characteristics and prognosis of crown fractures in immature permanent incisors due to trauma, and identify factors affecting their prognosisto provide a reference for clinical treatment. Methods This study was a
Externí odkaz:
https://doaj.org/article/c762988e339e41f89bf1a9f47c827b78
