In appropriate frameworks, automatic differentiation is transparent to the user, at the cost of
being a significant computational burden when the number of operations is large. For …

We introduce a method for the fast estimation of data-adapted, spatially and temporally
dependent regularization parameter-maps for variational image reconstruction, focusing on …

Variational regularization is commonly used to solve linear inverse problems, and involves
augmenting a data fidelity by a regularizer. The regularizer is used to promote a priori …

Full waveform inversion (FWI) is a standard algorithm in seismic imaging. It solves the
inverse problem of computing a model of the physical properties of the earth's subsurface by …

We consider a bilevel learning framework for learning linear operators. In this framework, the
learnable parameters are optimized via a loss function that also depends on the minimizer of …

Many optimization problems require hyperparameters, ie, parameters that must be pre-
specified in advance, such as regularization parameters and parametric regularizers in …

Full Waveform Inversion (FWI) is a standard algorithm in seismic imaging. Its implementation
requires the a priori choice of a number of" design parameters", such as the positions of …

Bilevel learning has gained prominence in machine learning, inverse problems, and
imaging applications, including hyperparameter optimization, learning data-adaptive …

Inverse problems are key issues in several scientific areas, including signal processing and
medical imaging. Data-driven approaches for inverse problems aim for learning model and …

Various tasks in data science are modeled utilizing the variational regularization approach,
where manually selecting regularization parameters presents a challenge. The difficulty gets …