AI RESEARCH
Variational Garrote for Sparse Inverse Problems
arXiv CS.LG
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ArXi:2603.12562v1 Announce Type: cross Sparse regularization plays a central role in solving inverse problems arising from incomplete or corrupted measurements. Different regularizers correspond to different prior assumptions about the structure of the unknown signal, and reconstruction performance depends on how well these priors match the intrinsic sparsity of the data.