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RADS uses reinforcement learning to pick the most informative samples for few-shot fine-tuning, boosting transfer-learning accuracy on low-resource, highly imbalanced clinical datasets.
University of Memphis researchers propose HAMR, a model-agnostic meta-learning framework that uses bi-level optimization and neighborhood-aware resampling to adaptively reweight hard examples and minority classes across six imbalanced NLP datasets.