What is recorded here?
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.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Original2016v1 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.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Dan Hendrycks; Kevin Gimpel
Source-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.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
GELU: Gaussian-CDF-weighted neural activation
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.
Scientific scope
Original2016v1 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.
Reading status
This 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.
People and systems
Dan Hendrycks; Kevin Gimpel
Source-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.