Patent ID: 11914955
Assignee: ROYAL BANK OF CANADA
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer implemented system for a variational autoencoder to conduct text sequence machine learning, the system comprising:
a computer memory; and
a processor operating in conjunction with the computer memory, the processor executing machine interpretable instruction sets stored on the memory to provide a variational autoencoder to conduct text sequence machine learning, the variational autoencoder having an encoder and a decoder, wherein the encoder reads an input sequence x=[x1, x2, . . . , xn] and generates a feature vector hxk for a series of hidden states hx=[h1, h2, . . . , hn] for the input sequence by performing pooling operations over multiple temporal dimensions of the hidden states, wherein the series of hidden states hx is arranged by the multiple temporal dimensions, wherein the feature vector hxk for the series of hidden states hx=[h1, h2, . . . , hn] has corresponding multiple temporal dimensions such that a temporal dimension of the feature vector hxk is referred to as a k-th dimension of the feature vector hxk, wherein the processor computes the k-th dimension of the feature vector hxk using a pooling operation of the k-th dimension of the hidden states, wherein the memory stores the feature vector for the series of hidden states hx; and
the processor further provides an approximate posterior data structure determination engine to extract from the feature vector for the series of hidden states hx, a mean and a variance parameter, and to encapsulate the mean and the variance parameter as an approximate posterior data structure for the variational autoencoder to conduct the text sequence machine learning;
wherein the variational autoencoder is a class of latent variable generative models; and
wherein the decoder is configured to generate output data based on the approximate posterior data structure and the feature vector for the series of hidden states hx.