Stochastic categorical autoencoder network

A computer system for generating a stochastic categorical autoencoder learning network, the computer system comprising:

• a set of one or more processor cores;
• and
• computer memory in communication with the set of processor cores, wherein the computer memory stores software that when executed by the set of processor cores, causes the set of processor cores to train the stochastic categorical autoencoder network by performing steps that comprise:
• training an encoder network to output, subject to one or more constraints, parameters for parametric probability distributions of sample random variables from input data, wherein:
• the parameters comprise measures of central tendency and measures of dispersion;
• and
• the one or more constraints comprise a first constraint that constrains a measure of a magnitude of a vector of the measures of central tendency as compared to a measure of a magnitude of a vector of the measures of dispersion;
• generating the sample random variables from the parameters;
• and
• training a decoder to output the input data from the sample random variables.