activity
20182022
most citedFairness and Accuracy in Federated Learning

36 citations · 43 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG20224 cited

Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction

Peng Xie, Minbo Ma, Tianrui Li +4

Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, it faces two main challenges. First,…

cs.LG20223 cited

A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting

Benhan Li, Shengdong Du, Tianrui Li

Time series forecasting is widely used in the fields of equipment life cycle forecasting, weather forecasting, traffic flow forecasting, and other fields. Recently, some scholars h…

cs.LG2022

Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting

Jiabin Tang, Tang Qian, Shijing Liu +3

Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and ur…

cs.LG202036 cited

Fairness and Accuracy in Federated Learning

Wei Huang, Tianrui Li, Dexian Wang +2

In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure priv…

cs.LG2019

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,…

cs.LG2018

Deep Air Quality Forecasting Using Hybrid Deep Learning Framework

Shengdong Du, Tianrui Li, Yan Yang +1

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air qu…