Zobrazeno 1 - 10
of 292
pro vyhledávání: '"Wang Shenhao"'
Autor:
Tang, Yihong, Qu, Ao, Wang, Zhaokai, Zhuang, Dingyi, Wu, Zhaofeng, Ma, Wei, Wang, Shenhao, Zheng, Yunhan, Zhao, Zhan, Zhao, Jinhua
Vision language models (VLMs) have demonstrated impressive performance across a wide range of downstream tasks. However, their proficiency in spatial reasoning remains limited, despite its crucial role in tasks involving navigation and interaction wi
Externí odkaz:
http://arxiv.org/abs/2410.16162
Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly problematic in high stakes applications where accurate uncertainty esti
Externí odkaz:
http://arxiv.org/abs/2410.09570
Quantifying uncertainty is crucial for robust and reliable predictions. However, existing spatiotemporal deep learning mostly focuses on deterministic prediction, overlooking the inherent uncertainty in such prediction. Particularly, highly-granular
Externí odkaz:
http://arxiv.org/abs/2409.08766
Publikováno v:
Meikuang Anquan, Vol 52, Iss 9, Pp 218-223, 230 (2021)
In order to improve the efficiency and simplicity of mining area collapse prediction, it is proposed to use neural network algorithm for analysis and prediction. Eight hazard factors including hydrological characteristics, geological structure, final
Externí odkaz:
https://doaj.org/article/126f0cbbf82546adbb6e2e3c79586f07
Autor:
Zhuang, Dingyi, Wang, Qingyi, Zheng, Yunhan, Guo, Xiaotong, Wang, Shenhao, Koutsopoulos, Haris N, Zhao, Jinhua
Transportation mode share analysis is important to various real-world transportation tasks as it helps researchers understand the travel behaviors and choices of passengers. A typical example is the prediction of communities' travel mode share by acc
Externí odkaz:
http://arxiv.org/abs/2405.14079
Autor:
Feng, Siqi, Yao, Rui, Hess, Stephane, Daziano, Ricardo A., Brathwaite, Timothy, Walker, Joan, Wang, Shenhao
Publikováno v:
Transportation Research Part C: Emerging Technologies, Volume 166, 2024, 104767
Deep neural networks (DNNs) frequently present behaviorally irregular patterns, significantly limiting their practical potentials and theoretical validity in travel behavior modeling. This study proposes strong and weak behavioral regularities as nov
Externí odkaz:
http://arxiv.org/abs/2404.14701
Autor:
Gao, Xiaowei, Jiang, Xinke, Zhuang, Dingyi, Chen, Huanfa, Wang, Shenhao, Law, Stephen, Haworth, James
Traffic accidents present substantial challenges to human safety and socio-economic development in urban areas. Developing a reliable and responsible traffic accident prediction model is crucial to addressing growing public safety concerns and enhanc
Externí odkaz:
http://arxiv.org/abs/2309.05072
Publikováno v:
Horticultural Plant Journal, Vol 1, Iss 1, Pp 29-34 (2015)
Tubercles and spines on fruit peel are important commercial traits in cucumber (Cucumis sativus L.). From an ethyl methane sulfonate cucumber mutant library, we discovered a new glabrous mutant that bears no tubercle or spine on fruit peel and fewer
Externí odkaz:
https://doaj.org/article/2bb38be90b1c4b268fca89f5033913a8
Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems. However, real-world transportation data, collected from loop detectors or similar sources, often contains missing values (MVs)
Externí odkaz:
http://arxiv.org/abs/2305.06480
Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems. However, previous travel demand forecasting research predominantly focused on improving prediction accuracy, ign
Externí odkaz:
http://arxiv.org/abs/2303.05698