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Self-Learnable Activation Functions: Revolutionizing Neural Network Understanding.pdf

1906.09529.pdf
Preview of Self-Learnable Activation Functions: Revolutionizing Neural Network Understanding
🔗 Source: arxiv.org
📊 Size: 448 KB
📄 Pages: 18 pages
⬇️ Downloads: 27

Summary

Self-Learnable Activation Functions (SLAF) are learned during training and can approximate most existing activation functions. SLAF is a weighted sum of pre-defined basis elements, allowing a search in the entire space of continuous functions. Various training routines can be used to achieve performance with SLAF-equipped neural networks (SLNNs). SLNNs can approximate any neural network with Lipschitz continuous activations, to any arbitrary error, highlighting their capacity and possible equivalence with standard NNs. SLNNs can be completely represented as a collection of finite degree polynomials up to the last layer, obviating several hyperparameters like width and depth. Using SLAF along with standard activations can provide performance improvements with only a small increase in the number of parameters.

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  • File Format: PDF
  • File Size: 448 KB
  • Pages: 18
  • Language: EN
  • Total Downloads: 27
  • Last Updated: 7 hours ago

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