Junchen Ye

Orcid: 0000-0003-2677-0751

According to our database1, Junchen Ye authored at least 17 papers between 2019 and 2025.

Collaborative distances:

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Links

On csauthors.net:

Bibliography

2025
TS-MAE: A masked autoencoder for time series representation learning.
Inf. Sci., 2025

2024
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

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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