114 citations · 848 across the 42 of their papers we have counts for
14 papers · 1 filter
Audit and Improve Robustness of Private Neural Networks on Encrypted Data
Jiaqi Xue, Lei Xu, Lin Chen +3
Performing neural network inference on encrypted data without decryption is one popular method to enable privacy-preserving neural networks (PNet) as a service. Compared with regul…
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Huan Zhang, Shiqi Wang, Kaidi Xu +5
Bound propagation methods, when combined with branch and bound, are among the most effective methods to formally verify properties of deep neural networks such as correctness, robu…
Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression
Sheng Lin, Wei Jiang, Wei Wang +4
Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices.…
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations
Hao Cheng, Kaidi Xu, Chenan Wang +3
To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximi…
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification
Shiqi Wang, Huan Zhang, Kaidi Xu +4
Bound propagation based incomplete neural network verifiers such as CROWN are very efficient and can significantly accelerate branch-and-bound (BaB) based complete verification of…
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Ren Wang, Kaidi Xu, Sijia Liu +4
Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. It enables us to learn a meta-initialization} of model…
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Kaidi Xu, Sijia Liu, Pin-Yu Chen +4
Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. As a result, there is a surge of interest in using these models for making poten…
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
Kaidi Xu, Zhouxing Shi, Huan Zhang +6
Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has…
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
Xiangyi Chen, Sijia Liu, Kaidi Xu +4
The adaptive momentum method (AdaMM), which uses past gradients to update descent directions and learning rates simultaneously, has become one of the most popular first-order optim…
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
Sijia Liu, Songtao Lu, Xiangyi Chen +5
In this paper, we study the problem of constrained robust (min-max) optimization ina black-box setting, where the desired optimizer cannot access the gradients of the objective fun…
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,…
Brain-inspired reverse adversarial examples
Shaokai Ye, Sia Huat Tan, Kaidi Xu +3
A human does not have to see all elephants to recognize an animal as an elephant. On contrast, current state-of-the-art deep learning approaches heavily depend on the variety of tr…
Progressive Weight Pruning of Deep Neural Networks using ADMM
Shaokai Ye, Tianyun Zhang, Kaiqi Zhang +10
Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices…
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
Kaidi Xu, Sijia Liu, Pu Zhao +6
When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and a…