PRISM forms predictions as sparse mixtures of learned prototypes trained with clustering objectives, matching dense model accuracy while enabling ~500x faster data attribution and behavior editing without finetuning.
Large-scale training data attribution for music generative models via unlearning
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ARIA decomposes music training data attribution into musical aspects and supplies reliability diagnostics from similarity metrics and score matrix analysis, with validation on symbolic models using counterfactual retraining.
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ARIA: A Diagnostic Framework for Music Training Data Attribution
ARIA decomposes music training data attribution into musical aspects and supplies reliability diagnostics from similarity metrics and score matrix analysis, with validation on symbolic models using counterfactual retraining.