2 citations · 4 across the 5 of their papers we have counts for
5 papers
FedHC: A Scalable Federated Learning Framework for Heterogeneous and Resource-Constrained Clients
Min Zhang, Fuxun Yu, Yongbo Yu +3
Federated Learning (FL) is a distributed learning paradigm that empowers edge devices to collaboratively learn a global model leveraging local data. Simulating FL on GPU is essenti…
Solving polynomial variational inequality problems via Lagrange multiplier expressions and Moment-SOS relaxations
Jiawang Nie, Defeng Sun, Xindong Tang +1
In this paper, we study variational inequality problems (VIPs) with involved mappings and feasible sets characterized by polynomial functions (namely, polynomial VIPs). We propose…
Boosting Verified Training for Robust Image Classifications via Abstraction
Zhaodi Zhang, Zhiyi Xue, Yang Chen +4
This paper proposes a novel, abstraction-based, certified training method for robust image classifiers. Via abstraction, all perturbed images are mapped into intervals before feedi…
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks
Xingwu Guo, Ziwei Zhou, Yueling Zhang +2
Occlusion is a prevalent and easily realizable semantic perturbation to deep neural networks (DNNs). It can fool a DNN into misclassifying an input image by occluding some segments…
Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks
Zhaodi Zhang, Yiting Wu, Si Liu +2
The robustness of deep neural networks is crucial to modern AI-enabled systems and should be formally verified. Sigmoid-like neural networks have been adopted in a wide range of ap…