3 papers
cs.LG2025
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…
cs.LG2025
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…
math.NA2025
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…