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RMT4ELM

A Random Matrix Approach to Extreme Learning Machine

This page contains a simple demo using Python 3 of the theoretical results in the following paper:

A Random Matrix Approach to Neural Networks

where recent advances in matrix matrix theory are used to analyze the performance of randomly-connected single-layer neural nets (also referred in literatures as extreme learning machines).

About the code

Comparison between theory and practice is available for data from

  • MNIST database
  • Gaussian mixture model

for a dozen of commonly-used activation functions.

Dependencies

To be able to test this code requires the following:

We strongly recommend you to use Jupyter nootbook to have a direct illustration within your web browsers: here.

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