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Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too
Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and dense models. We study novel connections between downstream performance of compressed models and the ability to retain gradient information via influ
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