42 citations · 161 across the 16 of their papers we have counts for
5 papers · 1 filter
S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
Zhihang Yuan, Bingzhe Wu, Zheng Liang +3
Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN). In contrast to static methods (e.g. weight p…
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Bingzhe Wu, Chaochao Chen, Shiwan Zhao +6
Bayesian deep learning is recently regarded as an intrinsic way to characterize the weight uncertainty of deep neural networks~(DNNs). Stochastic Gradient Langevin Dynamics~(SGLD)…
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
Bingzhe Wu, Shiwan Zhao, ChaoChao Chen +5
In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. Theoretically, we prove th…
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Peichen Xie, Bingzhe Wu, Guangyu Sun
Recently, deep learning as a service (DLaaS) has emerged as a promising way to facilitate the employment of deep neural networks (DNNs) for various purposes. However, using DLaaS a…
P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification
Bingzhe Wu, Shiwan Zhao, Guangyu Sun +4
Recently, deep convolutional neural networks (CNNs) have achieved great success in pathological image classification. However, due to the limited number of labeled pathological ima…