An observed value consistent diffusion model for imputing missing values in multivariate time series
Missing values, which are common in multivariate time series, is most important obstacle
towards the utilization and interpretation of those data. Great efforts have been employed on …
towards the utilization and interpretation of those data. Great efforts have been employed on …
Frigate: Frugal spatio-temporal forecasting on road networks
Modelling spatio-temporal processes on road networks is a task of growing importance.
While significant progress has been made on developing spatio-temporal graph neural …
While significant progress has been made on developing spatio-temporal graph neural …
Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective
With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …
data is continuously collected through streaming manners, which has propelled the …
Autostl: Automated spatio-temporal multi-task learning
Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling
multiple spatio-temporal tasks can further promote an intelligent city life by integrating their …
multiple spatio-temporal tasks can further promote an intelligent city life by integrating their …
Graph Neural Processes for Spatio-Temporal Extrapolation
We study the task of spatio-temporal extrapolation that generates data at target locations
from surrounding contexts in a graph. This task is crucial as sensors that collect data are …
from surrounding contexts in a graph. This task is crucial as sensors that collect data are …
A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework
Spatiotemporal data forecasting is a fundamental task in the field of graph data mining.
Typical spatiotemporal data prediction methods usually capture spatial dependencies by …
Typical spatiotemporal data prediction methods usually capture spatial dependencies by …
Countering modal redundancy and heterogeneity: a self-correcting multimodal fusion
Fusing multimodal heterogeneous data plays a vital role in recognition and prediction tasks
in various fields, eg, action recognition and traffic accident forecast. Yet, there remain some …
in various fields, eg, action recognition and traffic accident forecast. Yet, there remain some …
Multi-Scale Enhanced Features Correlation Filters Learning with Dual Second-Order Difference for UAV Tracking
Currently, most Discriminative Correlation Filters (DCF) algorithms used for Unmanned
Aerial Vehicle (UAV) target tracking primarily focus on improving the tracking model …
Aerial Vehicle (UAV) target tracking primarily focus on improving the tracking model …
Condition-Guided Urban Traffic Co-Prediction With Multiple Sparse Surveillance Data
Traffic prediction is one of the important research directions in Intelligent Transportation
Systems, with positive implications for vehicle dispatching and vehicle management. In …
Systems, with positive implications for vehicle dispatching and vehicle management. In …
When Imbalance Meets Imbalance: Structure-driven Learning for Imbalanced Graph Classification
W Xu, P Wang, Z Zhao, B Wang, X Wang… - Proceedings of the ACM …, 2024 - dl.acm.org
Graph Neural Networks (GNNs) can learn representative graph-level features to achieve
efficient graph classification. But GNNs usually assume an environment where both class …
efficient graph classification. But GNNs usually assume an environment where both class …