IGLU: The Integrated Gaussian Linear Unit Activation Function
Activation functions are fundamental to deep neural networks, governing gradient flow, optimization stability, and representational capacity. Within historic deep architectures, while ReLU has been the dominant choice for the activation function, modern transformer-based models increasingly are adopting smoother alternatives such as GELU and other self-gated alternatives. Despite their empirical success, the mathematical relationships among these functions and the principles underlying their effectiveness remains only partially understood. We introduce IGLU, a parametric activation function derived as a scale mixture of GELU gates under a half-normal mixing distribution. This derivation y...
| Authors | Mingi Kang, Zai Yang, Jeova Farias Sales Rocha Neto |
|---|---|
| Primary category | cs.LG |
| Published | 6 March 2026 |
| Last updated at source | 12 June 2026 |
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