134 results about "Latent variable" patented technology

Systems and methods for data augmentation in a neural network system includes performing a first training process, using a first training dataset on a neural network system including an autoencoder including an encoder and a decoder to generate a trained autoencoder. A trained encoder is configured to receive a first plurality of input data in an N-dimensional data space and generate a first plurality of latent variables in an M-dimensional latent space, wherein M is an integer less than N. A sampling process is performed on the first plurality of latent variables to generate a first plurality of latent variable samples. A trained decoder is used to generate a second training dataset using the first plurality of latent variable samples. The second training dataset is used to train a first classifier including a first classifier neural network model to generate a trained classifier for providing transaction classification.

A computer-implemented method and a respective system for anomaly detection and / or predictive maintenance is provided. The method comprises: receiving a new observation characterizing at least one parameter of the entity; inputting the new observation to a deep neural network (100) having a plurality of hidden layers and being trained using a training data set that includes possible observations; obtaining a second set of intermediate output values that are output from at least one of the plurality of hidden layers of the deep neural network by inputting the received new observation to the deep neural network; mapping, using a latent variable model stored in a storage medium, the second set of intermediate output values to a second set of projected values; determining whether or not the received new observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values, calculating, by the deep neural network, a prediction for the new observation; and determining a result indicative of the occurrence of at least one anomaly in the entity based on the prediction and the determination whether or not the new observation is an outlier. The latent variable model stored in the storage medium is constructed by obtaining first sets of intermediate output values that are output from said one of the plurality of hidden layers of the deep neural network, each of the first sets of intermediate output values obtained by inputting a different one of the possible observations included in at least a part of the training dataset; and constructing the latent variable model using the first sets of intermediate output values, the latent variable model providing a mapping of the first sets of intermediate output values to first sets of projected values in a sub-space of the latent variable model that has a dimension lower than a dimension of the sets of the intermediate outputs.