447 citations · 782 across the 31 of their papers we have counts for
7 papers · 1 filter
A Hypergradient Approach to Robust Regression without Correspondence
Yujia Xie, Yixiu Mao, Simiao Zuo +4
We consider a variant of regression problem, where the correspondence between input and output data is not available. Such shuffled data is commonly observed in many real world pro…
Deep Parallel MRI Reconstruction Network Without Coil Sensitivities
Wanyu Bian, Yunmei Chen, Xiaojing Ye
We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function…
Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction
Yunmei Chen, Hongcheng Liu, Xiaojing Ye +1
We propose a general learning based framework for solving nonsmooth and nonconvex image reconstruction problems. We model the regularization function as the composition of the $l_{…
Network Diffusions via Neural Mean-Field Dynamics
Shushan He, Hongyuan Zha, Xiaojing Ye
We propose a novel learning framework based on neural mean-field dynamics for inference and estimation problems of diffusion on networks. Our new framework is derived from the Mori…
A Novel Learnable Gradient Descent Type Algorithm for Non-convex Non-smooth Inverse Problems
Qingchao Zhang, Xiaojing Ye, Hongcheng Liu +1
Optimization algorithms for solving nonconvex inverse problem have attracted significant interests recently. However, existing methods require the nonconvex regularization to be sm…
Numerical Solution of Inverse Problems by Weak Adversarial Networks
Gang Bao, Xiaojing Ye, Yaohua Zang +1
We consider a weak adversarial network approach to numerically solve a class of inverse problems, including electrical impedance tomography and dynamic electrical impedance tomogra…
Learning Cost Functions for Optimal Transport
Shaojun Ma, Haodong Sun, Xiaojing Ye +2
Inverse optimal transport (OT) refers to the problem of learning the cost function for OT from observed transport plan or its samples. In this paper, we derive an unconstrained con…