12 citations · 58 across the 33 of their papers we have counts for
4 papers · 1 filter
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization
Bairu Hou, Jinghan Jia, Yihua Zhang +4
Robustness evaluation against adversarial examples has become increasingly important to unveil the trustworthiness of the prevailing deep models in natural language processing (NLP…
On the Robustness of deep learning-based MRI Reconstruction to image transformations
Jinghan Jia, Mingyi Hong, Yimeng Zhang +2
Although deep learning (DL) has received much attention in accelerated magnetic resonance imaging (MRI), recent studies show that tiny input perturbations may lead to instabilities…
CLAWSAT: Towards Both Robust and Accurate Code Models
Jinghan Jia, Shashank Srikant, Tamara Mitrovska +4
We integrate contrastive learning (CL) with adversarial learning to co-optimize the robustness and accuracy of code models. Different from existing works, we show that code obfusca…
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Yimeng Zhang, Yuguang Yao, Jinghan Jia +4
The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs). Thereby, robustif…