  In this paper, we show that the performance of a learnt generative model is
closely related to the model's ability to accurately represent the inferred
\textbf{latent data distribution}, i.e. its topology and structural properties.
We propose LaDDer to achieve accurate modelling of the latent data distribution
in a variational autoencoder framework and to facilitate better representation
learning. The central idea of LaDDer is a meta-embedding concept, which uses
multiple VAE models to learn an embedding of the embeddings, forming a ladder
of encodings. We use a non-parametric mixture as the hyper prior for the
innermost VAE and learn all the parameters in a unified variational framework.
From extensive experiments, we show that our LaDDer model is able to accurately
estimate complex latent distribution and results in improvement in the
representation quality. We also propose a novel latent space interpolation
method that utilises the derived data distribution.
