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Kafnets: Kernel-based non-parametric activation functions for neural networks.

Simone Scardapane1, Steven Van Vaerenbergh2, Simone Totaro3

  • 1Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.

Neural Networks : the Official Journal of the International Neural Network Society
|November 28, 2018
PubMed
Summary
This summary is machine-generated.

Researchers introduce novel flexible activation functions for neural networks, called kernel activation functions (KAFs). These adaptable functions offer enhanced flexibility and approximation capabilities, addressing limitations in current neural network designs.

Keywords:
Activation functionsKernel methodsNeural networks

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Neural networks typically use fixed nonlinear activation functions, limiting model flexibility.
  • Existing methods for adaptable activation functions have not achieved widespread adoption.
  • Research into more flexible activation functions remains an open area.

Purpose of the Study:

  • To introduce a novel family of flexible activation functions: kernel activation functions (KAFs).
  • To explore variations in designing and initializing KAFs, including multidimensional schemes.
  • To provide an overview of alternative adaptable activation function techniques.

Main Methods:

  • Developed flexible activation functions based on kernel expansions at each neuron.
  • Proposed multiple design and initialization strategies for KAFs.
  • Investigated multidimensional KAFs for nonlinear information combination.

Main Results:

  • KAFs can approximate any convex or non-convex mapping over a subset of real numbers.
  • KAFs are smooth, linear in parameters, and amenable to regularization (e.g., L1 for sparsity).
  • KAFs satisfy properties not simultaneously met by other known models.

Conclusions:

  • The proposed kernel activation functions offer significant flexibility and desirable mathematical properties.
  • KAFs represent a promising advancement in neural network activation function design.
  • Experimental validation supports the efficacy of the proposed KAFs.