6 citations · 11 across the 6 of their papers we have counts for
5 papers · 1 filter
On the convergence of doubly stochastic block gradient descent method for linear inverse problems
Yuxin Xia, Zehui Zhou
Stochastic gradient descent (SGD) and randomized block coordinate descent (RBCD) belong to two fundamentally different classes of stochastic iterative regularization methods, and a…
On the convergence of stochastic variance reduced gradient for linear inverse problems
Bangti Jin, Zehui Zhou
Stochastic variance reduced gradient (SVRG) is an accelerated version of stochastic gradient descent based on variance reduction, and is promising for solving large-scale inverse p…
On the Convergence of A Data-Driven Regularized Stochastic Gradient Descent for Nonlinear Ill-Posed Problems
Zehui Zhou
Stochastic gradient descent (SGD) is a promising method for solving large-scale inverse problems, due to its excellent scalability with respect to data size. In this work, we analy…
On the Approximation of Bi-Lipschitz Maps by Invertible Neural Networks
Bangti Jin, Zehui Zhou, Jun Zou
Invertible neural networks (INNs) represent an important class of deep neural network architectures that have been widely used in several applications. The universal approximation…
An Analysis of Stochastic Variance Reduced Gradient for Linear Inverse Problems
Bangti Jin, Zehui Zhou, Jun Zou
Stochastic variance reduced gradient (SVRG) is a popular variance reduction technique for accelerating stochastic gradient descent (SGD). We provide a first analysis of the method…