48 citations · 85 across the 6 of their papers we have counts for
9 papers
Deep Ensemble as a Gaussian Process Approximate Posterior
Zhijie Deng, Feng Zhou, Jianfei Chen +2
Deep Ensemble (DE) is an effective alternative to Bayesian neural networks for uncertainty quantification in deep learning. The uncertainty of DE is usually conveyed by the functio…
Accurate and Reliable Forecasting using Stochastic Differential Equations
Peng Cui, Zhijie Deng, Wenbo Hu +1
It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments. Although a lot of efforts have been made,…
Black-box Detection of Backdoor Attacks with Limited Information and Data
Yinpeng Dong, Xiao Yang, Zhijie Deng +4
Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments. A malicious backdoor could be embedded in a model by…
LiBRe: A Practical Bayesian Approach to Adversarial Detection
Zhijie Deng, Xiao Yang, Shizhen Xu +2
Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples. Various adversarial defense strategies have been proposed to resolve t…
Adversarial Distributional Training for Robust Deep Learning
Yinpeng Dong, Zhijie Deng, Tianyu Pang +2
Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples. However, most existing AT method…
Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure
Zhijie Deng, Yucen Luo, Jun Zhu +1
Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights. However, such models face the challenge of Bayes…