Samsung Electronics Co., Ltd.

Qualcomm Incorporated

Jongha Ryu

Jungwon Lee

Mostafa El-Khamy

Yoo Jin Choi

Yang Yang

Taco Sebastiaan Cohen

Young-Han Kim

Guillaume Konrad Sautière

A method and system are herein disclosed. The method includes developing a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables, generating a variational posterior of the joint latent variable model, training the variational posterior, and performing inference of the first variable from the second variable based on the variational posterior.

Method and apparatus for stochastic inference between multiple random variables via common representation

A system and method for operating a neural network. In some embodiments, the neural network includes a variational autoencoder, and the training of the neural network includes training the variational autoencoder with a plurality of samples of a first random variable; and a plurality of samples of a second random variable, the plurality of samples of the first random variable and the plurality of samples of the second random variable being unpaired, the training of the neural network including updating weights in the neural network based on a first loss function, the first loss function being based on a measure of deviation from consistency between: a conditional generation path from the first random variable to the second random variable, and a conditional generation path from the second random variable to the first random variable.

Method and apparatus for learning stochastic inference models between multiple random variables with unpaired data

A device includes one or more processors configured to generate, at an encoder portion of an autoencoder, first output data at least partially based on first input data and to generate, at a decoder portion or the autoencoder, a representation of the first input data at least partially based on the first output data. The one or more processors are configured to generate, at the encoder portion, second output data based on second input data and first state data and to generate, at the decoder portion, a representation of the second input data based on the second output data and second state data. Each of the first state data and the second state data correspond to the state of the decoder portion resulting from generation of the representation of the first input data. The first and second input data correspond to sequential values of a signal to be encoded.

Method and apparatus for recurrent auto-encoding

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Jongha Ryu