  Learning a generative model from partial data (data with missingness) is a
challenging area of machine learning research. We study a specific
implementation of the Auto-Encoding Variational Bayes (AEVB) algorithm, named
in this paper as a Variational Auto-Decoder (VAD). VAD is a generic framework
which uses Variational Bayes and Markov Chain Monte Carlo (MCMC) methods to
learn a generative model from partial data. The main distinction between VAD
and Variational Auto-Encoder (VAE) is the encoder component, as VAD does not
have one. Using a proposed efficient inference method from a multivariate
Gaussian approximate posterior, VAD models allow inference to be performed via
simple gradient ascent rather than MCMC sampling from a probabilistic decoder.
This technique reduces the inference computational cost, allows for using more
complex optimization techniques during latent space inference (which are shown
to be crucial due to a high degree of freedom in the VAD latent space), and
keeps the framework simple to implement. Through extensive experiments over
several datasets and different missing ratios, we show that encoders cannot
efficiently marginalize the input volatility caused by imputed missing values.
We study multimodal datasets in this paper, which is a particular area of
impact for VAD models.
