Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …

Most models in machine learning contain at least one hyperparameter to control for model
complexity. Choosing an appropriate set of hyperparameters is both crucial in terms of …

Inverse Problems in medical imaging and computer vision are traditionally solved using
purely model-based methods. Among those variational regularization models are one of the …

In this paper, we present a new deep learning framework for 3-D tomographic
reconstruction. To this end, we map filtered back-projection-type algorithms to neural …

Over the last decades, the total variation (TV) has evolved to be one of the most broadly-
used regularisation functionals for inverse problems, in particular for imaging applications …

Hyperparameter optimization can be formulated as a bilevel optimization problem, where
the optimal parameters on the training set depend on the hyperparameters. We aim to adapt …

The paper studies the solution of stochastic optimization problems in which approximations
to the gradient and Hessian are obtained through subsampling. We first consider Newton …

Medical image reconstruction (MIR) is the elementary way of producing an internal 3D view
of the patient. MIR is inherently ill-posed, and various approaches have been proposed to …

This review discusses methods for learning parameters for image reconstruction problems
using bilevel formulations. Image reconstruction typically involves optimizing a cost function …

In this work, we describe a new approach that uses deep neural networks (DNN) to obtain
regularization parameters for solving inverse problems. We consider a supervised learning …