332 citations · 895 across the 68 of their papers we have counts for
6 papers · 1 filter
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Jeet Mohapatra, Tsui-Wei, Weng +3
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task. While current verification methods mainly focus on the -norm t…
Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation
Yuh-Shyang Wang, Tsui-Wei Weng, Luca Daniel
Deep neural networks are known to be fragile to small adversarial perturbations. This issue becomes more critical when a neural network is interconnected with a physical system in…
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
Kaidi Xu, Hongge Chen, Sijia Liu +4
Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification. However,…
POPQORN: Quantifying Robustness of Recurrent Neural Networks
Ching-Yun Ko, Zhaoyang Lyu, Tsui-Wei Weng +3
The vulnerability to adversarial attacks has been a critical issue for deep neural networks. Addressing this issue requires a reliable way to evaluate the robustness of a network.…
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen +3
With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important…
Finite-Sum Smooth Optimization with SARAH
Lam M. Nguyen, Marten van Dijk, Dzung T. Phan +3
The total complexity (measured as the total number of gradient computations) of a stochastic first-order optimization algorithm that finds a first-order stationary point of a finit…