90 citations · 256 across the 21 of their papers we have counts for
5 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…
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…
A Universal Logic Operator for Interpretable Deep Convolution Networks
KamWoh Ng, Lixin Fan, Chee Seng Chan
Explaining neural network computation in terms of probabilistic/fuzzy logical operations has attracted much attention due to its simplicity and high interpretability. Different cho…
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure
Caglar Aytekin, Francesco Cricri, Lixin Fan +1
For a graph representation of a dataset, a straightforward normality measure for a sample can be its graph degree. Considering a weighted graph, degree of a sample is the sum of th…
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network
Lixin Fan
We revisit fuzzy neural network with a cornerstone notion of generalized hamming distance, which provides a novel and theoretically justified framework to re-interpret many useful…