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Achieving robust regression of general ReLUs with a dramatically reduced query complexity could redefine the efficiency of active learning strategies.
Achieving a prediction error that scales with \(Δ^{1/3}\) reveals a surprising limitation in learning dynamics under Massart noise, challenging previous assumptions about optimal performance.
Out-of-domain self-supervised pretraining on brain MRIs beats in-domain supervised learning when generalizing to real-world clinical data.
