What is recorded here?
Elfwing and colleagues studied sigmoid-weighted linear and derivative activations with eligibility-trace RL; Ramachandran and colleagues later evaluated the same self-gated form as Swish and a scaled-parameter variant in deep vision/language networks.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
One lineage package with two independent source bylines, not two inventions of x*sigmoid(x). February2017original calls the units SiL/SiLd1; OctoberSwishv1 is a self-gated activation report, not the later activation-search paper/title. No absolute first-ever priority adjudication or universal superiority; inherited EE-RBM/gating and separate RL algorithm choices retained.
Read the original work
- Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning
- Swish: a Self-Gated Activation Function
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
Stefan Elfwing; Eiji Uchibe; Kenji Doya; Prajit Ramachandran; Barret Zoph; Quoc V. Le
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
Sigmoid-weighted activation lineage: SiL/SiLd1 and Swish
Elfwing and colleagues studied sigmoid-weighted linear and derivative activations with eligibility-trace RL; Ramachandran and colleagues later evaluated the same self-gated form as Swish and a scaled-parameter variant in deep vision/language networks.
Scientific scope
One lineage package with two independent source bylines, not two inventions of x*sigmoid(x). February2017original calls the units SiL/SiLd1; OctoberSwishv1 is a self-gated activation report, not the later activation-search paper/title. No absolute first-ever priority adjudication or universal superiority; inherited EE-RBM/gating and separate RL algorithm choices retained.
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
Stefan Elfwing; Eiji Uchibe; Kenji Doya; Prajit Ramachandran; Barret Zoph; Quoc V. Le
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.