3 citations · 5 across the 4 of their papers we have counts for
4 papers
Stochastic Gradient Descent for Nonlinear Inverse Problems in Banach Spaces
Bangti Jin, Zeljko Kereta, Yuxin Xia
Stochastic gradient descent (SGD) and its variants are widely used and highly effective optimization methods in machine learning, especially for neural network training. By using a…
Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography
Alexander Denker, Fabio Margotti, Jianfeng Ning +5
Electrical Impedance Tomography (EIT) is a powerful imaging modality widely used in medical diagnostics, industrial monitoring, and environmental studies. The EIT inverse problem i…
Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches
Derick Nganyu Tanyu, Jianfeng Ning, Andreas Hauptmann +2
Electrical Impedance Tomography (EIT) is a powerful imaging technique with diverse applications, e.g., medical diagnosis, industrial monitoring, and environmental studies. The EIT…
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction
Riccardo Barbano, Alexander Denker, Hyungjin Chung +5
Denoising diffusion models have emerged as the go-to generative framework for solving inverse problems in imaging. A critical concern regarding these models is their performance on…