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Forget hand-tuning loss functions: this meta-learning approach automatically learns optimal sample reweighting for sparse additive models, boosting robustness and accuracy.
Escaping the curse of noisy data in semi-supervised learning: S$^2$MAM adaptively selects features and tunes similarity metrics, leading to more robust and interpretable models.
LLMs are still far from being able to generate expert-level clinical guidelines, despite advances in deep research systems.
