It is enough to answer 3 of the 5 questions below:
1)PSD: Is there an advantage of using PSD (predictive sparse decomposition) instead of Sparse Autoencoders where PSD seems to be harder to optimize.
1.1) If you want to stack PSD’s, what you would give as an input to the upper layer? f_{\alpha}(x^t) or h^t.
2)In the energy function of the Boltzmann machine where does the 1/2 in front of quadratic terms come from?
3) Is there an experiment in the literature comparing the “Greedy layerwise unsupervised training” vs “Jointly training all the layers of stacked autoencoder”?
4) In the Hinton’s bottleneck architecture, does it make sense to use a Contractive autoencoder. Because of its contraction property.
Representation Learning 