June 5, 2024, 4:45 a.m. | Martin Burger, Samira Kabri

cs.LG updates on arXiv.org arxiv.org

arXiv:2312.09845v2 Announce Type: replace-cross
Abstract: In this chapter we provide a theoretically founded investigation of state-of-the-art learning approaches for inverse problems from the point of view of spectral reconstruction operators. We give an extended definition of regularization methods and their convergence in terms of the underlying data distributions, which paves the way for future theoretical studies. Based on a simple spectral learning model previously introduced for supervised learning, we investigate some key properties of different learning paradigms for inverse problems, …

abstract art arxiv convergence cs.lg cs.na data definition insights investigation math.na operators regularization replace state terms type view

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