AutoEncoders
AUTOENCODERS There are many types of autoencoders. Standard AutoEncoders help to minimize the L2 reconstruction error used for images. The sparse autoencoders minimize L(x, g f x + Ω(z), where Ω is a sparsity penalty. It can be used for application where a small set of variables are expected to explain a process. The denoising minimizes the L(x, g f ~x ) where ~x is a corrupted version of X. These denoising autoencoders learns mechanisms from removing noise from observations. Contract Autoencoders Minimize L(x, g f x + Ω(z, x), where Ω(z, x) = This forces the encoder to learn features that do not change much when slightly perturbing data. The stochastic encoders and decoders encode probabilities such as p(z|X) and p(X|z) and minimize likelihoods. In deep autoencoders, layers use a Restricted Boltzmann machine, then fine-tuned using backpropagation. In deep autoencoders, dataset of curves are randomly generated by selecting 3 points from the plane. A CNN-b...
