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Explicit geometric priors in image restoration can outperform traditional methods while using fewer parameters, challenging the reliance on implicit regularization.
Almost-sure consistency and optimal convergence rates for sparse function recovery reveal critical insights into statistical inverse learning under noise and indirect observations.
