27 citations · 38 across the 4 of their papers we have counts for
6 papers
The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues
J. Waghorne, C. Howard, H. Hu +4
Complex soft tissues, for example the knee meniscus, play a crucial role in mobility and joint health, but when damaged are incredibly difficult to repair and replace. This is due…
Boosting Adversarial Transferability through Enhanced Momentum
Xiaosen Wang, Jiadong Lin, Han Hu +2
Deep learning models are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations on benign images. Many existing adversarial attack metho…
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps
Yujin Huang, Han Hu, Chunyang Chen
Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition. To make it more accessible to…
Ontology-based Interpretable Machine Learning for Textual Data
Phung Lai, NhatHai Phan, Han Hu +3
In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. D…
An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere
Han Hu, NhatHai Phan, James Geller +4
As the problem of drug abuse intensifies in the U.S., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use…
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
NhatHai Phan, My T. Thai, Han Hu +3
In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to ad…