Variational Autoencoder

Learning Multi-Layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference

Ran experiments for a short-run MCMC residual network that outperforms a variational autoencoder in terms of image reconstruction error and image synthesis quality, while not requiring the design of a separate inference network

Formulation of Variational Lower Bound and Application of VAE to MNIST Dataset

Formulation of the evidence lower bound (ELBO) for the variational autoencoder and an application to synthesizing binary images