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A Matlab implementation of the Soft Committee Machine and learning algorithms + an implementation of differential equations for the thermodynamic limit. Ideal for comparing theoretical results with simulations.
Jupyter Notebook MATLAB Mathematica Python M
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Simulation Dropout.ipynb


A description of the repository and usage of the scripts is following soon.

Upcoming functionality:

  • Add the Swish activation function in the simulation and potentially in the theory. Experiment with ReLU vs. Swish learning a rule defined by a sigmoidal Erf teacher.
  • Drift processes and weight decay.
  • Exact theoretical on-line ReLU dynamics for general learning rate, if possible.
  • Hidden-to-output weights, additional layers, tree-like structures.
  • Other learning scenarios, batch learning.
  • Active learning
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