TS-MAE: A masked autoencoder for time series representation learning.
Inf. Sci., 2025
Adaptive Taxonomy Learning and Historical Patterns Modeling for Patent Classification.
ACM Trans. Inf. Syst., November, 2024
Dynamic Graph Representation Learning for Passenger Behavior Prediction.
Future Internet, August, 2024
MvTS-library: An open library for deep multivariate time series forecasting.
Knowl. Based Syst., January, 2024
Temporal Graph Network for continuous-time dynamic event sequence.
Knowl. Based Syst., 2024
Learning solid dynamics with graph neural network.
Inf. Sci., 2024
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
DyGKT: Dynamic Graph Learning for Knowledge Tracing.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
Repeat-Aware Neighbor Sampling for Dynamic Graph Learning.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
An NCDE-based Framework for Universal Representation Learning of Time Series.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
GAN-Based Anomaly Detection for Multivariate Time Series Using Polluted Training Set.
IEEE Trans. Knowl. Data Eng., December, 2023
Spatio-Temporal AutoEncoder for Traffic Flow Prediction.
IEEE Trans. Intell. Transp. Syst., May, 2023
Deep multi-task learning with flexible and compact architecture search.
Int. J. Data Sci. Anal., March, 2023
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data.
CoRR, 2021
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019