Methods › General › Adaptive Activation Functions › PELU
Parametric Exponential Linear Unit
PELU
Introduced by Ludovic Trottier et al. in Parametric Exponential Linear Unit for Deep Convolutional Neural Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Parameterized Exponential Linear Units, or PELU, is an activation function for neural networks. It involves learning a parameterization of ELU in order to learn the proper activation shape at each layer in a CNN.
The PELU has two additional parameters over the ELU:
f(x) = cx if x > 0 f(x) = αexp^(x/b) - 1 if x ≤0
Where a, b, and c > 0. Here c causes a change in the slope in the positive quadrant, b controls the scale of the exponential decay, and α controls the saturation in the negative quadrant.
Source: Activation Functions
Papers archive 2025-07-28
2 shown of 2, 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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Adaptive Rational Activations to Boost Deep Reinforcement Learning 18 Feb 2021 · 4 repositories · arXiv:2102.09407Syntology ran 6 of 6 samples · 0 unverified · 3 pointer-only (licence)
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Parametric Exponential Linear Unit for Deep Convolutional Neural Networks 30 May 2016 · 0 repositories · arXiv:1605.09332
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