Researchers have proposed a new structural interpretation of activation functions like GELU, ReLU, SiLU/Swish, and hard swish. This work views GELU not just as a stochastic gate output, but through a Gaussian complementary first-order loss function. This perspective generalizes to a family of threshold-transmission activations, offering a new way to understand their behavior. Experiments on vision and language models suggest that calibrated or learned uniform-threshold gates can be competitive with or outperform existing activations. AI
IMPACT This research offers a novel theoretical framework for understanding and potentially improving activation functions, which are fundamental components of neural networks.
RANK_REASON The cluster contains an academic paper detailing a new theoretical interpretation of existing machine learning activation functions.
- arXiv
- Gaussian error linear unit
- Gelu
- hard swish
- Hugging Face
- rectifier
- Silu Activation Function
- Swish
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