deepmat

Project Website: None

Github Link:

**Description**

= 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()