CELU (Continuously Differentiable Exponential Linear Unit) is a modification of ELU that ensures continuous first derivatives — addressing the non-differentiability of ELU at $x = 0$ when $alpha eq 1$ by using a scaled exponential formulation.
Properties of CELU
- Formula: $ ext{CELU}(x) = egin{cases} x & x > 0 \ alpha(exp(x/alpha) - 1) & x leq 0 end{cases}$
- $C^1$ Smoothness: Continuously differentiable everywhere, including at $x = 0$, for any $alpha > 0$.
- Parameterized: $alpha$ controls the saturation value and the smoothness for negative inputs.
- Paper: Barron (2017).
Why It Matters
- Mathematical Correctness: Fixes the differentiability issue of ELU when $alpha
eq 1$.
- Optimization: Smooth activations generally lead to smoother loss landscapes and easier optimization.
- Niche: Less widely adopted than GELU/Swish but theoretically well-motivated.
CELU is the mathematically correct ELU — ensuring smooth differentiability for any choice of the saturation parameter.
celuceluneural architecture
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