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    Artificial Intelligence

    ELU (Exponential Linear Unit)

    Also known as:
    Exponential Linear Unit
    ELU Activation
    Updated: 2/12/2026

    An activation function that exponentially dampens negative values toward a negative saturation value – smoother than ReLU with zero-mean outputs.

    Quick Summary

    ELU dampens negative values exponentially instead of cutting them – smoother than ReLU with natural zero-mean outputs.

    Explanation

    ELU: f(x) = x for x > 0, f(x) = α(eˣ - 1) for x ≤ 0. The exponential part ensures smooth gradients and zero-mean outputs. Slightly more expensive than ReLU due to exponential computation.

    Marketing Relevance

    ELU showed that zero-mean activations can partially replace batch normalization.

    Origin & History

    Clevert et al. (2015) introduced ELU and showed faster convergence than ReLU. SELU (2017) extended ELU with self-normalizing properties.

    Comparisons & Differences

    ELU (Exponential Linear Unit) vs. ReLU

    ReLU: non-smooth at 0, not zero-mean; ELU: smooth, zero-mean, but more expensive due to exponential.

    ELU (Exponential Linear Unit) vs. SELU

    ELU needs external normalization; SELU self-normalizes through special α/λ values – but needs specific initialization.

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