102 citations · 214 across the 8 of their papers we have counts for
8 papers
Stationary Point Losses for Robust Model
Weiwei Gao, Dazhi Zhang, Yao Li +2
The inability to guarantee robustness is one of the major obstacles to the application of deep learning models in security-demanding domains. We identify that the most commonly use…
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation
Fan Yin, Yao Li, Cho-Jui Hsieh +1
Adversarial Examples Detection (AED) is a crucial defense technique against adversarial attacks and has drawn increasing attention from the Natural Language Processing (NLP) commun…
A Review of Adversarial Attack and Defense for Classification Methods
Yao Li, Minhao Cheng, Cho-Jui Hsieh +1
Despite the efficiency and scalability of machine learning systems, recent studies have demonstrated that many classification methods, especially deep neural networks (DNNs), are v…
Adversarial Examples Detection with Bayesian Neural Network
Yao Li, Tongyi Tang, Cho-Jui Hsieh +1
In this paper, we propose a new framework to detect adversarial examples motivated by the observations that random components can improve the smoothness of predictors and make it e…
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Yao Li, Martin Renqiang Min, Wenchao Yu +3
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples. Inspired by the observation that the intrinsic dimension of i…
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
Xuanqing Liu, Yao Li, Chongruo Wu +1
We present a new algorithm to train a robust neural network against adversarial attacks. Our algorithm is motivated by the following two ideas. First, although recent work has demo…
Adversarial 3D Human Pose Estimation via Multimodal Depth Supervision
Kun Zhou, Jinmiao Cai, Yao Li +5
In this paper, a novel deep-learning based framework is proposed to infer 3D human poses from a single image. Specifically, a two-phase approach is developed. We firstly utilize a…
Uncertainty Quantification for High Dimensional Sparse Nonparametric Additive Models
Qi Gao, Randy C. S. Lai, Thomas C. M. Lee +1
Statistical inference in high dimensional settings has recently attracted enormous attention within the literature. However, most published work focuses on the parametric linear re…