no code implementations • 18 Mar 2024 • Yile Chen, Xiucheng Li, Gao Cong, Zhifeng Bao, Cheng Long
In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance of various time-sensitive downstream tasks.
1 code implementation • 15 Mar 2024 • Xinli Hao, Yile Chen, Chen Yang, Zhihui Du, Chaohong Ma, Chao Wu, Xiaofeng Meng
However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms.
no code implementations • 22 Dec 2023 • Tangwen Qian, Yile Chen, Gao Cong, Yongjun Xu, Fei Wang
However, the development of multi-source domain generalization in this task presents two notable issues: (1) negative transfer; (2) inadequate modeling for external factors.
no code implementations • 28 Feb 2022 • Yile Chen, Xiucheng Li, Gao Cong, Cheng Long, Zhifeng Bao, Shang Liu, Wanli Gu, Fuzheng Zhang
As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers.
no code implementations • 29 Sep 2021 • Yawen Chen, Zeyi Wen, Yile Chen, Jian Chen, Jin Huang
However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training.
1 code implementation • 18 Sep 2020 • Kwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Minyang Wan, Diego Martinez, Xia Hu
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications.