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      "statement": "# Representer points: signed training-feature contributions to network logits\n\nDerives a representer decomposition of pre-activation outputs into signed training-feature similarities at stationary points with positively weighted L2 regularization of the final linear layer.\n\n## Scientific scope\n\nThe theorem is conditional on its final-layer regularization and stationarity assumptions, not arbitrary unregularized checkpoints. Signed logit contributions are not identical to loss influence or deletion counterfactuals. First two authors explicitly equal; proof source read, not independently certified.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nChih-Kuan Yeh; Joon Sik Kim; Ian E. H. Yen; Pradeep Ravikumar\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Representer Point Selection for Explaining Deep Neural Networks](https://arxiv.org/pdf/1811.09720v1)\n",
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