56 citations · 283 across the 30 of their papers we have counts for
6 papers · 1 filter
Mathematical Analysis of Adversarial Attacks
Zehao Dou, Stanley J. Osher, Bao Wang
In this paper, we analyze efficacy of the fast gradient sign method (FGSM) and the Carlini-Wagner's L2 (CW-L2) attack. We prove that, within a certain regime, the untargeted FGSM c…
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,…
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…
Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning
Wei Zhu, Qiang Qiu, Bao Wang +3
Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the f…
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
Bao Wang, Xiyang Luo, Fangbo Zhang +3
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scal…
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…