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University of Twente
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A new DRO framework that tailors uncertainty modeling to the data-acquisition process significantly boosts robustness and interpretability in learned reconstructions.
Infinite neural nets can be sparse, and this paper proves it, showing that total variation regularization provably yields sparse solutions in infinite-width shallow ReLU networks, with sparsity bounds tied to the geometry of the data.
