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

Claim 9:
10. A computer implemented method for providing a variational autoencoder for conducting text sequence machine learning using, the method comprising:
providing, using a processor, a variational autoencoder to conduct text sequence machine learning, the variational autoencoder having an encoder and a decoder;
reading an input sequence x=[x1, x2, . . . , xn] using the encoder provided by the processor that accesses memory storing the input sequence;
generating a feature vector hxk for a series of hidden states hx=[h1, h2, . . . , hn] using the encoder provided by the processor to read the hidden states from the memory and perform pooling operations over multiple temporal dimensions of all 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; and
extracting from the series of hidden states hx, a mean and a variance parameter using the processor and encapsulating 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;
generating, using the decoder, output data based on the approximate posterior data structure and the feature vector for the series of hidden states hx.