activity
20152023
most citedSelf-supervised GAN: Analysis and Improvement with Multi-class Minimax Game

41 citations · 63 across the 6 of their papers we have counts for

collaborators

6 papers

stat.AP202119 cited

A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition

Linxiao Yang, Qingsong Wen, Bo Yang +1

Many real-world time series exhibit multiple seasonality with different lengths. The removal of seasonal components is crucial in numerous applications of time series, including fo…

cs.CV201941 cited

Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game

Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen +2

Self-supervised (SS) learning is a powerful approach for representation learning using unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) traini…

stat.ML2018

Low-Rank Phase Retrieval via Variational Bayesian Learning

Kaihui Liu, Jiayi Wang, Zhengli Xing +2

In this paper, we consider the problem of low-rank phase retrieval whose objective is to estimate a complex low-rank matrix from magnitude-only measurements. We propose a hierarchi…

cs.LG20171 cited

Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning

Hang Xiao, Zhengli Xing, Linxiao Yang +2

In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method…

cs.IT2016

Channel Estimation for Millimeter Wave Multiuser MIMO Systems via PARAFAC Decomposition

Zhou Zhou, Jun Fang, Linxiao Yang +3

We consider the problem of uplink channel estimation for millimeter wave (mmWave) systems, where the base station (BS) and mobile stations (MSs) are equipped with large antenna arr…

cs.LG20152 cited

Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model

Linxiao Yang, Jun Fang, Hong Cheng +1

We consider a dictionary learning problem whose objective is to design a dictionary such that the signals admits a sparse or an approximate sparse representation over the learned d…