70 citations · 111 across the 8 of their papers we have counts for
8 papers
VPNets: Volume-preserving neural networks for learning source-free dynamics
Aiqing Zhu, Beibei Zhu, Jiawei Zhang +2
We propose volume-preserving networks (VPNets) for learning unknown source-free dynamical systems using trajectory data. We propose three modules and combine them to obtain two net…
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
Zhuohang Li, Jiaxin Zhang, Luyang Liu +1
Federated Learning (FL) framework brings privacy benefits to distributed learning systems by allowing multiple clients to participate in a learning task under the coordination of a…
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates
Zhuohang Li, Luyang Liu, Jiaxin Zhang +1
Federated Learning (FL) enables multiple distributed clients (e.g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the clie…
Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge
Yong Dai, Jian Liu, Jian Zhang +2
Sentiment analysis (SA) is an important research area in cognitive computation-thus in-depth studies of patterns of sentiment analysis are necessary. At present, rich resource data…
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis
Yong Dai, Jian Liu, Xiancong Ren +1
Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target…
Free-riders in Federated Learning: Attacks and Defenses
Jierui Lin, Min Du, Jian Liu
Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages th…
Latent Dirichlet Allocation in Generative Adversarial Networks
Lili Pan, Shen Cheng, Jian Liu +2
We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs). Despite the success of existing methods, they often ignore the un…
Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction
Yunzhe Tao, Lin Ma, Weizhong Zhang +3
Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Ex…