3 papers
math.NA2026
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…
eess.IV2025
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…
cs.CV2025
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…