112 citations · 440 across the 16 of their papers we have counts for
6 papers · 1 filter
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…