Methods › General › Activation Functions › CELU
Continuously Differentiable Exponential Linear Units
CELU
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Exponential Linear Units (ELUs) are a useful rectifier for constructing deep learning architectures, as they may speed up and otherwise improve learning by virtue of not have vanishing gradients and by having mean activations near zero. However, the ELU activation as parametrized in [1] is not continuously differentiable with respect to its input when the shape parameter alpha is not equal to 1. We present an alternative parametrization which is C1 continuous for all values of alpha, making the rectifier easier to reason about and making alpha easier to tune. This alternative parametrization has several other useful properties that the original parametrization of ELU does not: 1) its derivative with respect to x is bounded, 2) it contains both the linear transfer function and ReLU as special cases, and 3) it is scale-similar with respect to alpha. CELU(x) = max(0,x) + min(0, α* (exp(x/α) - 1))
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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Modelowanie nieliniowej charakterystyki szerokopasmowych wzmacniaczy radiowych o zmiennym napięciu zasilania; Modeling Nonlinear Characteristics of Wideband Radio Frequency Amplifiers with Variable Supply Voltage 5 Mar 2025 · 0 repositories · arXiv:2503.03648
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Continuously Differentiable Exponential Linear Units 24 Apr 2017 · 1 repository · arXiv:1704.07483
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