206 citations · 953 across the 33 of their papers we have counts for
5 papers · 1 filter
Urban flows prediction from spatial-temporal data using machine learning: A survey
Peng Xie, Tianrui Li, Jia Liu +3
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors,…
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
Huaishao Luo, Tianrui Li, Bing Liu +1
This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity…
Federated Forest
Yang Liu, Yingting Liu, Zhijie Liu +3
Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the Eu…
Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks
Junkai Sun, Junbo Zhang, Qiaofei Li +3
Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public…
UrbanFM: Inferring Fine-Grained Urban Flows
Yuxuan Liang, Kun Ouyang, Lin Jing +5
Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-l…