2 citations · 4 across the 4 of their papers we have counts for
5 papers
Edge Graph Neural Networks for Massive MIMO Detection
Hongyi Li, Junxiang Wang, Yongchao Wang
Massive Multiple-Input Multiple-Out (MIMO) detection is an important problem in modern wireless communication systems. While traditional Belief Propagation (BP) detectors perform p…
A Convergent ADMM Framework for Efficient Neural Network Training
Junxiang Wang, Hongyi Li, Liang Zhao
As a well-known optimization framework, the Alternating Direction Method of Multipliers (ADMM) has achieved tremendous success in many classification and regression applications. R…
Community-based Layerwise Distributed Training of Graph Convolutional Networks
Hongyi Li, Junxiang Wang, Yongchao Wang +2
The Graph Convolutional Network (GCN) has been successfully applied to many graph-based applications. Training a large-scale GCN model, however, is still challenging: Due to the no…
Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework
Junxiang Wang, Hongyi Li, Zheng Chai +3
While Graph Neural Networks (GNNs) are popular in the deep learning community, they suffer from several challenges including over-smoothing, over-squashing, and gradient vanishing.…
Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization
Junxiang Wang, Hongyi Li, Liang Zhao
In recent years, even though Stochastic Gradient Descent (SGD) and its variants are well-known for training neural networks, it suffers from limitations such as the lack of theoret…