11 citations · 29 across the 7 of their papers we have counts for
7 papers · 1 filter
Deep Learning and Foundation Models for Weather Prediction: A Survey
Jimeng Shi, Azam Shirali, Bowen Jin +10
Physics-based numerical models have been the bedrock of atmospheric sciences for decades, offering robust solutions but often at the cost of significant computational resources. De…
IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning
Jiawen Qin, Haonan Yuan, Qingyun Sun +8
Deep graph learning has gained grand popularity over the past years due to its versatility and success in representing graph data across a wide range of domains. However, the perva…
Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates
Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang +2
Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of ext…
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Zekun Cai, Renhe Jiang, Xinyu Yang +5
Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city. However, with the dramat…
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
Renhe Jiang, Zhaonan Wang, Jiawei Yong +6
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity…
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Renhe Jiang, Zhaonan Wang, Jiawei Yong +6
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity…
Event-Aware Multimodal Mobility Nowcasting
Zhaonan Wang, Renhe Jiang, Hao Xue +3
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios…