A Neural-Network-Based Convex Regularizer for Image Reconstruction. - Alexis Goujon; Sebastian Neumayer; Pakshal Bohra; Stanislas Ducotterd; Michael Unser (CoRR, 2022)

A Neural-Network-Based Convex Regularizer for Inverse Problems. - Alexis Goujon; Sebastian Neumayer; Pakshal Bohra; Stanislas Ducotterd; Michael Unser (IEEE Trans. Computational Imaging, 2023)

Learning a Convex Patch-Based Synthesis Model via Deep Equilibrium. - Stanislas Ducotterd; Sebastian Neumayer; Michael Unser (ICASSP, 2024)

Parseval Convolution Operators and Neural Networks. - Michael Unser; Stanislas Ducotterd (CoRR, 2024)

Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures. - Michael Unser; Alexis Goujon; Stanislas Ducotterd (CoRR, 2024)

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions. - Stanislas Ducotterd; Alexis Goujon; Pakshal Bohra; Dimitris Perdios; Sebastian Neumayer; Michael Unser (J. Mach. Learn. Res., 2024)

Learning of Patch-Based Smooth-Plus-Sparse Models for Image Reconstruction. - Stanislas Ducotterd; Sebastian Neumayer; Michael Unser (CPAL, 2025)

Universal Architectures for the Learning of Polyhedral Norms and Convex Regularization Functionals. - Michael Unser; Stanislas Ducotterd (CoRR, 2025)