116 citations · 196 across the 22 of their papers we have counts for
7 papers · 1 filter
How Neural Architectures Affect Deep Learning for Communication Networks?
Yifei Shen, Jun Zhang, Khaled B. Letaief
In recent years, there has been a surge in applying deep learning to various challenging design problems in communication networks. The early attempts adopt neural architectures in…
Learn to Communicate with Neural Calibration: Scalability and Generalization
Yifan Ma, Yifei Shen, Xianghao Yu +3
The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. U…
How Powerful is Graph Convolution for Recommendation?
Yifei Shen, Yongji Wu, Yao Zhang +4
Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empir…
Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation
Yifan Ma, Yifei Shen, Xianghao Yu +3
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have…
AI Empowered Resource Management for Future Wireless Networks
Yifei Shen, Jun Zhang, S. H. Song +1
Resource management plays a pivotal role in wireless networks, which, unfortunately, leads to challenging NP-hard problems. Artificial Intelligence (AI), especially deep learning t…
Invariant Information Bottleneck for Domain Generalization
Bo Li, Yifei Shen, Yezhen Wang +6
Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear…
Decentralized Statistical Inference with Unrolled Graph Neural Networks
He Wang, Yifei Shen, Ziyuan Wang +4
In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples with…