Reference implementation for a variational autoencoder in TensorFlow and PyTorch ... Variational inference is used to fit the model to binarized MNIST handwritten digits images. An inference network ...

In this paper, we develop a mixture model that contains two components to address ... The second component is the latent group preference component based on variational autoencoder, a deep generative ...

In practical chemical production processes, nonlinear dynamic multirate data is widespread and challenging to model. This paper innovatively proposes a temporal–spatial pyramid variational autoencoder ...

Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio ... Architecture v1 Original continuous model (minimum GPU memory : 8Go) v2 Improved continuous model ...