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Argonne National Laboratory
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NTK regression can require exponentially more samples than necessary for compositional tasks, revealing a critical gap in our understanding of neural network performance.
Flood mapping accuracy can be dramatically improved by leveraging a novel cross-sensor learning approach that integrates SAR and optical imagery, achieving AUPRC scores above 0.95.
