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      "statement": "# Self-normalizing networks: SELU moment stabilization\n\nDesigned scaled exponential-linear activations and compatible initialization to attract layerwise activation mean/variance toward a stable region under an analyzed moment map.\n\n## Scientific scope\n\nGeneral ML architecture despite author affiliation and incidental application examples; no biological results entered. Moment analysis uses independent/common-moment inputs and Gaussian preactivation approximation with bounded parameter regions. Not universal self-normalization for arbitrary correlated architectures/weights, nor proof that all gradients can never vanish or explode. Only selected pages of102pagev1read.\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\nGünter Klambauer; Thomas Unterthiner; Andreas Mayr; Sepp Hochreiter\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- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515v1)\n",
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