Methods › General › Activation Functions › ELiSH
Exponential Linear Squashing Activation
ELiSH
Introduced by Mina Basirat et al. in The Quest for the Golden Activation Function
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The Exponential Linear Squashing Activation Function, or ELiSH, is an activation function used for neural networks. It shares common properties with Swish, being made up of an ELU and a Sigmoid:
f(x) = x/(1+e⁻ˣ) if x ≥0 f(x) = (eˣ - 1)/(1+e⁻ˣ) if x < 0
The Sigmoid part of ELiSH improves information flow, while the linear parts solve issues of vanishing gradients.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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The Quest for the Golden Activation Function 2 Aug 2018 · 0 repositories · arXiv:1808.00783
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Classification | 1 |
| image-classification | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections