69 citations · 518 across the 94 of their papers we have counts for
5 papers · 1 filter
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
Bao Wang, Binjie Yuan, Zuoqiang Shi +1
Empirical adversarial risk minimization (EARM) is a widely used mathematical framework to robustly train deep neural nets (DNNs) that are resistant to adversarial attacks. However,…
Error estimation of weighted nonlocal Laplacian on random point cloud
Zuoqiang Shi, Bao Wang, Stanley J. Osher
We analyze the convergence of the weighted nonlocal Laplacian (WNLL) on high dimensional randomly distributed data. The analysis reveals the importance of the scaling weight $μ\sim…
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization
Bao Wang, Alex T. Lin, Wei Zhu +3
We improve the robustness of Deep Neural Net (DNN) to adversarial attacks by using an interpolating function as the output activation. This data-dependent activation remarkably imp…
Deep Neural Nets with Interpolating Function as Output Activation
Bao Wang, Xiyang Luo, Zhen Li +3
We replace the output layer of deep neural nets, typically the softmax function, by a novel interpolating function. And we propose end-to-end training and testing algorithms for th…
Weighted Nonlocal Total Variation in Image Processing
Haohan Li, Zuoqiang Shi, Xiaoping Wang
In this paper, a novel weighted nonlocal total variation (WNTV) method is proposed. Compared to the classical nonlocal total variation methods, our method modifies the energy funct…