58 citations · 98 across the 11 of their papers we have counts for
18 papers
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Mengting Xu, Tao Zhang, Zhongnian Li +1
Efficient and effective attacks are crucial for reliable evaluation of defenses, and also for developing robust models. Projected Gradient Descent (PGD) attack has been demonstrate…
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack
Mengting Xu, Tao Zhang, Zhongnian Li +2
Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images. Recent studies have shown d…
Improving the Certified Robustness of Neural Networks via Consistency Regularization
Mengting Xu, Tao Zhang, Zhongnian Li +1
A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to b…
Transport based Graph Kernels
Kai Ma, Peng Wan, Daoqiang Zhang
Graph kernel is a powerful tool measuring the similarity between graphs. Most of the existing graph kernels focused on node labels or attributes and ignored graph hierarchical stru…
Shared Space Transfer Learning for analyzing multi-site fMRI data
Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang +2
Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing diff…
Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset
Muhammad Yousefnezhad, Jeffrey Sawalha, Alessandro Selvitella +1
Similarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by differen…