Multi-Scale Subgraph Contrastive Learning

Autor: Liu, Yanbei, Zhao, Yu, Wang, Xiao, Geng, Lei, Xiao, Zhitao

Graph-level contrastive learning, aiming to learn the representations for each graph by contrasting two augmented graphs, has attracted considerable attention. Previous studies usually simply assume that a graph and its augmented graph as a positive

Externí odkaz: http://arxiv.org/abs/2403.02719

Cross-scale contrastive triplet networks for graph representation learning

Autor: Liu, Yanbei, Shan, Wanjin, Wang, Xiao, Xiao, Zhitao, Geng, Lei, Zhang, Fang, Du, Dongdong, Pang, Yanwei

Publikováno v: In Pattern Recognition January 2024 145

On the Performance of Massive MIMO Systems With Low-Resolution ADCs Over Rician Fading Channels

Autor: Liu, Tianle, Tong, Jun, Guo, Qinghua, Xi, Jiangtao, Yu, Yanguang, Xiao, Zhitao

This paper considers uplink massive multiple-input multiple-output (MIMO) systems with lowresolution analog-to-digital converters (ADCs) over Rician fading channels. Maximum-ratio-combining (MRC) and zero-forcing (ZF) receivers are considered under t

Externí odkaz: http://arxiv.org/abs/1906.09841

Linear Shrinkage Estimation of Covariance Matrices Using Low-Complexity Cross-Validation

Autor: Tong, Jun, Hu, Rui, Xi, Jiangtao, Xiao, Zhitao, Guo, Qinghua, Yu, Yanguang

Publikováno v: J. Tong, R. Hu, J. Xi, Z. Xiao, Q. Guo, and Y. Yu, "Linear shrinkage estimation of covariance matrices using low-complexity cross-validation," Signal Processing, vol.148, pp. 223-233, July 2018

Shrinkage can effectively improve the condition number and accuracy of covariance matrix estimation, especially for low-sample-support applications with the number of training samples smaller than the dimensionality. This paper investigates parameter

Externí odkaz: http://arxiv.org/abs/1810.08360

Pathological Voice Detection and Classification Based on Multimodal Transmission Network

Autor: Geng, Lei ^*, Liang, Yan, Shan, Hongfeng, Xiao, Zhitao ^*, Wang, Wei, Wei, Mei

Publikováno v: In Journal of Voice November 2022