2 citations · 3 across the 4 of their papers we have counts for
6 papers
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning
Hua Huang, Fanhua Shang, Yuanyuan Liu +1
Federated Learning (FL) has become an active and promising distributed machine learning paradigm. As a result of statistical heterogeneity, recent studies clearly show that the per…
Learned Interpretable Residual Extragradient ISTA for Sparse Coding
Lin Kong, Wei Sun, Fanhua Shang +2
Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number of experiments as well as some theories have p…
Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
Qigong Sun, Xiufang Li, Fanhua Shang +4
The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage. Thus, DNNs cannot be efficiently applied to mobile phones and…
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling
Hongying Liu, Peng Zhao, Zhubo Ruan +2
Video super-resolution (VSR) aims at restoring a video in low-resolution (LR) and improving it to higher-resolution (HR). Due to the characteristics of video tasks, it is very impo…
Boosting Gradient for White-Box Adversarial Attacks
Hongying Liu, Zhenyu Zhou, Fanhua Shang +3
Deep neural networks (DNNs) are playing key roles in various artificial intelligence applications such as image classification and object recognition. However, a growing number of…
signADAM: Learning Confidences for Deep Neural Networks
Dong Wang, Yicheng Liu, Wenwo Tang +4
In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks. We first introduce the sign operation of stochastic gradients (as in sign-based…