Implementing a deep autoencoder is possible but requires a lot of effort. A result from the Universal Approximation Theorem (sometimes called the Cybenko Theorem) states, loosely speaking, that a ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

Within the past few years, models that can predict the structure or function of proteins have been widely used for a variety ...

Sparse autoencoders work by adjusting how a protein is represented within a neural network. Typically, a given protein will be represented by a pattern of activation of a constrained number of neurons ...