6 citations · 6 across the 6 of their papers we have counts for
5 papers · 1 filter
A Multi-Step Inertial ADMM Algorithm for Federated Learning
Hao Li, Qingsong Wang, Xihong Yan +2
Federated learning has recently emerged as an important paradigm for large-scale distributed model training, which can be naturally formulated as a class of distributed learning pr…
Decoupled Structure-Feature Alignment via Alternating Optimization for Graph Learning
Chengcheng Yan, Feifei Zhao, Dai Zhu +2
Conventional Graph Neural Networks (GNNs) couple feature transformation and neighborhood aggregation, which often renders them vulnerable to topological noise and heterophilous con…
Graph Convolutional Network With Pattern-Spatial Interactive and Regional Awareness for Traffic Forecasting
Xinyu Ji, Chengcheng Yan, Jibiao Yuan +1
Traffic forecasting is significant for urban traffic management, intelligent route planning, and real-time flow monitoring. Recent advances in spatial-temporal models have markedly…
Neural Network Training via Stochastic Alternating Minimization with Trainable Step Sizes
Chengcheng Yan, Jiawei Xu, Zheng Peng +1
The training of deep neural networks is inherently a nonconvex optimization problem, yet standard approaches such as stochastic gradient descent (SGD) require simultaneous updates…
A Triple-Inertial Accelerated Alternating Optimization Method for Deep Learning Training
Chengcheng Yan, Jiawei Xu, Qingsong Wang +1
The stochastic gradient descent (SGD) algorithm has achieved remarkable success in training deep learning models. However, it has several limitations, including susceptibility to v…