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      "statement": "# GELU: Gaussian-CDF-weighted neural activation\n\nDefined a smooth input-weighted activation through the expected action of an input-dependent stochastic identity/zero map, with Gaussian cumulative probabilities and empirical neural-network evaluations.\n\n## Scientific scope\n\nOriginal2016v1 title differs from later Gaussian Error Linear Units title. The deterministic expected map is not dropout randomness during inference; its Gaussian motivation is not a guarantee that every layer is normally distributed. No universal superiority, earliest claim for every sigmoid-weighted variant or independent approximation-theorem certification.\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\nDan Hendrycks; Kevin Gimpel\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- [Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units](https://arxiv.org/abs/1606.08415v1)\n",
      "summary": "Defined a smooth input-weighted activation through the expected action of an input-dependent stochastic identity/zero map, with Gaussian cumulative probabilities and empirical neural-network evaluations.",
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