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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.
Out-of-domain self-supervised pretraining on brain MRIs beats in-domain supervised learning when generalizing to real-world clinical data.
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.
