Zobrazeno 1 - 10
of 585
pro vyhledávání: '"Li, Ziyue"'
Autor:
Gou, Xiaochuan, Li, Ziyue, Lan, Tian, Lin, Junpeng, Li, Zhishuai, Zhao, Bingyu, Zhang, Chen, Wang, Di, Zhang, Xiangliang
Long-separated research has been conducted on two highly correlated tracks: traffic and incidents. Traffic track witnesses complicating deep learning models, e.g., to push the prediction a few percent more accurate, and the incident track only studie
Externí odkaz:
http://arxiv.org/abs/2407.11477
Natural gas demand is a crucial factor for predicting natural gas prices and thus has a direct influence on the power system. However, existing methods face challenges in assessing the impact of shocks, such as the outbreak of the Russian-Ukrainian w
Externí odkaz:
http://arxiv.org/abs/2407.10878
Autor:
Jiang, Haoyuan, Xiong, Xuantang, Li, Ziyue, Mao, Hangyu, Sui, Guanghu, Ruan, Jingqing, Cheng, Yuheng, Wei, Hua, Ketter, Wolfgang, Zhao, Rui
Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the c
Externí odkaz:
http://arxiv.org/abs/2407.10811
Recent advancements in few-shot segmentation (FSS) have exploited pixel-by-pixel matching between query and support features, typically based on cross attention, which selectively activate query foreground (FG) features that correspond to the same-cl
Externí odkaz:
http://arxiv.org/abs/2407.09842
The recent rapid development of language models (LMs) has attracted attention in the field of time series, including multimodal time series modeling. However, we note that current time series multimodal methods are biased, often assigning a primary r
Externí odkaz:
http://arxiv.org/abs/2406.06620
Autor:
Liu, Chenxi, Xu, Qianxiong, Miao, Hao, Yang, Sun, Zhang, Lingzheng, Long, Cheng, Li, Ziyue, Zhao, Rui
The widespread adoption of scalable mobile sensing has led to large amounts of time series data for real-world applications. A fundamental application is multivariate time series forecasting (MTSF), which aims to predict future time series values bas
Externí odkaz:
http://arxiv.org/abs/2406.01638
CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control
Autor:
Ruan, Jingqing, Li, Ziyue, Wei, Hua, Jiang, Haoyuan, Lu, Jiaming, Xiong, Xuantang, Mao, Hangyu, Zhao, Rui
Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite an amount of congestion, ev
Externí odkaz:
http://arxiv.org/abs/2405.17152
In sophisticated existing Text-to-SQL methods exhibit errors in various proportions, including schema-linking errors (incorrect columns, tables, or extra columns), join errors, nested errors, and group-by errors. Consequently, there is a critical nee
Externí odkaz:
http://arxiv.org/abs/2405.09593
Time series data are ubiquitous across various domains, making time series analysis critically important. Traditional time series models are task-specific, featuring singular functionality and limited generalization capacity. Recently, large language
Externí odkaz:
http://arxiv.org/abs/2405.02358
Autor:
Lan, Tian, Li, Ziyue, Lin, Junpeng, Li, Zhishuai, Bai, Lei, Li, Man, Tsung, Fugee, Zhao, Rui, Zhang, Chen
Directed Acyclic Graphical (DAG) models efficiently formulate causal relationships in complex systems. Traditional DAGs assume nodes to be scalar variables, characterizing complex systems under a facile and oversimplified form. This paper considers t
Externí odkaz:
http://arxiv.org/abs/2404.13836