On the Convergence of Stochastic Gradient Descent for Nonlinear Ill-Posed Problems. - Bangti Jin; Zehui Zhou; Jun Zou 003 (SIAM J. Optim., 2020)

An Analysis of Stochastic Variance Reduced Gradient for Linear Inverse Problems. - Bangti Jin; Zehui Zhou; Jun Zou 003 (CoRR, 2021)

On the Saturation Phenomenon of Stochastic Gradient Descent for Linear Inverse Problems. - Bangti Jin; Zehui Zhou; Jun Zou 003 (SIAM/ASA J. Uncertain. Quantification, 2021)

Evaluation of GPM-IMERG Precipitation Product at Multiple Spatial and Sub-Daily Temporal Scales over Mainland China. - Zehui Zhou; Dekai Lu; Bin Yong; Zhehui Shen; Hao Wu; Lei Yu (Remote. Sens., 2023)

On the simultaneous recovery of two coefficients in the Helmholtz equation for inverse scattering problems via neural networks. - Zehui Zhou (CoRR, 2024)

On the approximation of bi-Lipschitz maps by invertible neural networks. - Bangti Jin; Zehui Zhou; Jun Zou 003 (Neural Networks, 2024)

Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data - a case study of Southwest basin. - Zehui Zhou; Jiaxin Jin; Bin Yong; Weidong Huang 003; Lei Yu; Peiqi Yang; Dianchen Sun (Int. J. Appl. Earth Obs. Geoinformation, 2025)

On the Convergence of a Data-Driven Regularized Stochastic Gradient Descent for Nonlinear Ill-Posed Problems. - Zehui Zhou (SIAM J. Imaging Sci., 2025)

On the recovery of two function-valued coefficients in the Helmholtz equation for inverse scattering problems via neural networks. - Zehui Zhou (Adv. Comput. Math., 2025)

On the recovery of two function-valued coefficients in the Helmholtz equation for inverse scattering problems via inverse Born series. - Fioralba Cakoni; Shixu Meng; Zehui Zhou (CoRR, 2025)