Junchen Ye

Orcid: 0000-0003-2677-0751

According to our database1, Junchen Ye authored at least 15 papers between 2019 and 2024.

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Bibliography

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

2023
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

2022
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

2021
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

2019
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


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