112 citations · 440 across the 16 of their papers we have counts for
16 papers
Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance
Ce Ju, Reinmar Josef Kobler, Cuntai Guan
The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input…
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis
Ce Ju, Cuntai Guan
The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces based on electroencephalography (EEG). Over the past few decades, the perform…
Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification
Ce Ju, Cuntai Guan
Deep learning (DL) has been widely investigated in a vast majority of applications in electroencephalography (EEG)-based brain-computer interfaces (BCIs), especially for motor imag…
Deep Optimal Transport for Domain Adaptation on SPD Manifolds
Ce Ju, Cuntai Guan
Recent progress in geometric deep learning has drawn increasing attention from the machine learning community toward domain adaptation on symmetric positive definite (SPD) manifold…
Ternary Hashing
Chang Liu, Lixin Fan, Kam Woh Ng +5
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the…
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness
Yilun Jin, Lixin Fan, Kam Woh Ng +2
Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed. While adversarial training (AT) is regarded as the most robust defen…
Survey: Geometric Foundations of Data Reduction
Ce Ju
This survey is written in summer, 2016. The purpose of this survey is to briefly introduce nonlinear dimensionality reduction (NLDR) in data reduction. The first two NLDR were resp…
Privacy Threats Against Federated Matrix Factorization
Dashan Gao, Ben Tan, Ce Ju +2
Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect suf…
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
Lixin Fan, Kam Woh Ng, Ce Ju +4
This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks. First, we propose to quantitatively measure the…
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
Ce Ju, Ruihui Zhao, Jichao Sun +11
Prevention of stroke with its associated risk factors has been one of the public health priorities worldwide. Emerging artificial intelligence technology is being increasingly adop…
Federated Transfer Learning for EEG Signal Classification
Ce Ju, Dashan Gao, Ravikiran Mane +3
The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lac…
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
Dashan Gao, Ce Ju, Xiguang Wei +3
Electroencephalography (EEG) classification techniques have been widely studied for human behavior and emotion recognition tasks. But it is still a challenging issue since the data…
Stochastic Inverse Reinforcement Learning
Ce Ju
The goal of the inverse reinforcement learning (IRL) problem is to recover the reward functions from expert demonstrations. However, the IRL problem like any ill-posed inverse prob…
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Ce Ju, Zheng Wang, Cheng Long +2
Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for intelligent and autonomous vehicles. Complex sce…
Representation Learning for Spatial Graphs
Zheng Wang, Ce Ju, Gao Cong +1
Recently, the topic of graph representation learning has received plenty of attention. Existing approaches usually focus on structural properties only and thus they are not suffici…
Socially Aware Kalman Neural Networks for Trajectory Prediction
Ce Ju, Zheng Wang, Xiaoyu Zhang
Trajectory prediction is a critical technique in the navigation of robots and autonomous vehicles. However, the complex traffic and dynamic uncertainties yield challenges in the ef…