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README.md

deepmat

WARNING: this is not my main code, and there is no warranty attached!

= Generative Stochastic Network =

  • A simple implementation of GSN according to (Bengio et al., 2013)

= Convolutional Neural Network =

  • A naive implementation (purely using Matlab)
  • Pooling: max (Jonathan Masci's code) and average
  • Not for serious use!

= Restricted Boltzmann Machine & Deep Belief Networks =

  • Binary/Gaussian Visible Units + Binary Hidden Units
  • Enhanced Gradient, Adaptive Learning Rate
  • Adadelta for RBM
  • Contrastive Divergence
  • (Fast) Persistent Contrastive Divergence
  • Parallel Tempering
  • DBN: Up-down Learning Algorithm

= Deep Boltzmann Machine =

  • Binary/Gaussian Visible Units + Binary Hidden Units
  • (Persistent) Contrastive Divergence
  • Enhanced Gradient, Adaptive Learning Rate
  • Two-stage Pretraining Algorithm (example)
  • Centering Trick (fixed center variables only)

= Denoising Autoencoder (Tied Weights) =

  • Binary/Gaussian Visible Units + Binary(Sigmoid)/Gaussian Hidden Units
  • tanh/sigm/relu nonlinearities
  • Shallow: sparsity, contractive, soft-sparsity (log-cosh) regularization
  • Deep: stochastic backprop
  • Adagrad, Adadelta

= Multi-layer Perceptron =

  • Stochastic Backpropagation, Dropout
  • tanh/sigm/relu nonlinearities
  • Adagrad, Adadelta
  • Balanced minibatches using crossvalind()

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Matlab Code for Restricted/Deep Boltzmann Machines and Autoencoders

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