Recommender systems play a significant role in information filtering and have been utilized in different scenarios, such as e-commerce and social media. With the prosperity of deep …
With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic growth. Forecasting air quality has become an increasingly important …
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world applications. They capture dynamic system measurements and are produced in vast …
Y Tian, X Zhao, W Huang - Neurocomputing, 2022 - Elsevier
Compared to traditional machine learning, deep learning can learn deeper abstract data representation and understand scattered data properties. It has gained considerable …
X Luo, J Yuan, Z Huang, H Jiang… - International …, 2023 - proceedings.mlr.press
Leading graph ordinary differential equation (ODE) models have offered generalized strategies to model interacting multi-agent dynamical systems in a data-driven approach …
Machine learning (ML) is powering a rapidly-increasing number of web applications. As a crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model …