most citedUMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Late-decoupled 3D Hierarchical Semantic Segmentation with Semantic Prototype Discrimination based Bi-branch Supervision

Shuyu Cao, Chongshou Li, Jie Xu +2

3D hierarchical semantic segmentation (3DHS) is crucial for embodied intelligence applications that demand a multi-grained and multi-hierarchy understanding of 3D scenes. Despite t…

cs.LG2025

Non-collective Calibrating Strategy for Time Series Forecasting

Bin Wang, Yongqi Han, Minbo Ma +4

Deep learning-based approaches have demonstrated significant advancements in time series forecasting. Despite these ongoing developments, the complex dynamics of time series make i…

cs.LG2025

CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values

Kai Tang, Ji Zhang, Hua Meng +5

Multivariate time series forecasting (MTSF) is a critical task with broad applications in domains such as meteorology, transportation, and economics. Nevertheless, pervasive missin…

cs.LG2024

Causally-Aware Unsupervised Feature Selection Learning

Zongxin Shen, Yanyong Huang, Dongjie Wang +3

Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intri…

cs.LG20242 cited

UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method

Peng Xie, Minbo Ma, Bin Wang +2

Accurate prediction of metro Origin-Destination (OD) flow is essential for the development of intelligent transportation systems and effective urban traffic management. Existing ap…