Ce Ju

According to our database, Ce Ju authored at least 21 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Deep Geodesic Canonical Correlation Analysis for Covariance-Based Neuroimaging Data.

Ce Ju, Reinmar J. Kobler, Liyao Tang, Cuntai Guan, Motoaki Kawanabe

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification.

Ce Ju, Cuntai Guan

IEEE Trans. Neural Networks Learn. Syst., December, 2023

Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance.

Ce Ju, Reinmar Josef Kobler, Cuntai Guan

Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

2022

Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis.

Ce Ju, Cuntai Guan

CoRR, 2022

Deep Optimal Transport on SPD Manifolds for Domain Adaptation.

Ce Ju, Cuntai Guan

CoRR, 2022

2021

Ternary Hashing.

Chang Liu, Lixin Fan, Kam Woh Ng, Yilun Jin, Ce Ju, Tianyu Zhang, Chee Seng Chan, Qiang Yang

CoRR, 2021

2020

Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks.

Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chang Liu, Chee Seng Chan, Qiang Yang

Proceedings of the Federated Learning - Privacy and Incentive, 2020

Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness.

Yilun Jin, Lixin Fan, Kam Woh Ng, Ce Ju, Qiang Yang

CoRR, 2020

Geometric Foundations of Data Reduction.

Ce Ju

CoRR, 2020

Privacy Threats Against Federated Matrix Factorization.

Dashan Gao, Ben Tan, Ce Ju, Vincent W. Zheng, Qiang Yang

CoRR, 2020

Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks.

Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chang Liu, Chee Seng Chan, Qiang Yang

CoRR, 2020

Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention.

Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, Ben Tan, Han Yu, Yuan Jin

CoRR, 2020

Interaction-aware Kalman Neural Networks for Trajectory Prediction.

Ce Ju, Zheng Wang, Cheng Long, Xiaoyu Zhang, Dong Eui Chang

Proceedings of the IEEE Intelligent Vehicles Symposium, 2020

Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes.

Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chee Seng Chan

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Federated Transfer Learning for EEG Signal Classification.

Ce Ju, Dashan Gao, Ravikiran Mane, Ben Tan, Yang Liu, Cuntai Guan

Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

2019

HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography.

Dashan Gao, Ce Ju, Xiguang Wei, Yang Liu, Tianjian Chen, Qiang Yang

CoRR, 2019

Stochastic Inverse Reinforcement Learning.

Ce Ju, Dong Eui Chang

CoRR, 2019

Interaction-aware Kalman Neural Networks for Trajectory Prediction.

Ce Ju, Zheng Wang, Cheng Long, Xiaoyu Zhang, Gao Cong, Dong Eui Chang

CoRR, 2019

Effective and Efficient Sports Play Retrieval with Deep Representation Learning.

Zheng Wang, Cheng Long, Gao Cong, Ce Ju

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Privacy-preserving Heterogeneous Federated Transfer Learning.

Dashan Gao, Yang Liu, Anbu Huang, Ce Ju, Han Yu, Qiang Yang

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

Representation Learning for Spatial Graphs.

Zheng Wang, Ce Ju, Gao Cong, Cheng Long

CoRR, 2018