6 papers
Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization
Chengcheng Yan, Qingsong Wang
The paper proposes a two‑view framework that aligns GNN embeddings with a structure‑free feature prior learned by an anchor network, and introduces a channel‑split adaptive gated G…
Accelerated Tensor Completion via Trace-Regularized Fully-Connected Tensor Network
Wenchao Xie, Qingsong Wang, Chengcheng Yan +1
The fully-connected tensor network (FCTN) decomposition has gained prominence in the field of tensor completion owing to its powerful capacity to capture the low-rank characteristi…
Efficient QR-Based CP Decomposition Acceleration via Restructured Dimension Tree and Customized Extrapolation
Wenchao Xie, Jiawei Xu, Zheng Peng +1
The canonical polyadic (CP) decomposition is one of the most widely used tensor decomposition techniques. The conventional CP decomposition algorithm combines alternating least squ…
Accelerated Proximal Dogleg Majorization for Sparse Regularized Quadratic Optimization Problem
Feifei Zhao, Qingsong Wang, Mingcai Ding +1
This paper addresses the problems of minimizing the sum of a quadratic function and a proximal-friendly nonconvex nonsmooth function. While the existing Proximal Dogleg Opportunist…
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…