Can we jointly learn variational models and solvers for inverse problems?

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We've just posted online a new preprint co-authored with Lucas Drumetz and Francois Rousseau on the joint learning of variational models and associated solvers to address inverse problems (e.g., inpainting, denoising, space-time interpolation, data assimilation,...) pdf on Arxiv / Associated code on OceaniX github