www.pudn.com > deepmat-master-for-spectral.zip > README.md, change:2014-07-28,size:1349b

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()