Monday, Aug 5: 8:50 AM - 9:05 AM
Invited Paper Session
Oregon Convention Center
VAE (Variational Auto Encoder) is a nonlinear factor model which is popularly used in AI tasks
such as generation of synthetic data. VAE models the distribution of given data as the sum of
a nonlinear transformation of latent factors and noise. In most cases, the noise term is assumed to
be Gaussian with the constant variance. In this research, we propose a VAE model where the noise
term also depends on the latent factor, which we call the heterogeneous VAE (H-VAE). We provide motivational
examples for H-VAE, theoretical justification and empirical comparisons with other existing VAE algorithms.