10 citations · 19 across the 7 of their papers we have counts for
9 papers
Federated Deep Learning with Bayesian Privacy
Hanlin Gu, Lixin Fan, Bowen Li +3
Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with…
StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding
Jinshan Zeng, Qi Chen, Yunxin Liu +2
The generation of stylish Chinese fonts is an important problem involved in many applications. Most of existing generation methods are based on the deep generative models, particul…
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization
Jinshan Zeng, Yixuan Zha, Ke Ma +1
The low-rank stochastic semidefinite optimization has attracted rising attention due to its wide range of applications. The nonconvex reformulation based on the low-rank factorizat…
Leveraging both Lesion Features and Procedural Bias in Neuroimaging: An Dual-Task Split dynamics of inverse scale space
Xinwei Sun, Wenjing Han, Lingjing Hu +2
The prediction and selection of lesion features are two important tasks in voxel-based neuroimage analysis. Existing multivariate learning models take two tasks equivalently and op…
Learning the mapping : the cost of finding the needle in a haystack
Jiefu Zhang, Leonardo Zepeda-Núñez, Yuan Yao +1
The task of using machine learning to approximate the mapping with seems to be a trivial one. Given the knowledge of the separa…
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Ke Ma, Jinshan Zeng, Qianqian Xu +3
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi…