Many posterior distributions take intractable forms and thus require variational inference where analytical solutions cannot be found. Variational Inference and Monte Carlo Markov Chains (MCMC) are established mechanism to approximate these intractable values. An alternative approach to sampling and optimisation for approximation is a direct mapping between the data and posterior distribution. This is made possible by recent advances in deep learning methods. Latent Dirichlet Allocation (LDA)

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... a model which offers an intractable posterior of this nature. In LDA latent topics are learnt over unlabelled documents to soft cluster the documents. This paper assesses the viability of learning latent topics leveraging an autoencoder (in the form of Autoencoding variational Bayes) and compares the mimicked posterior distributions to that achieved by VI. After conducting various experiments the proposed AEVB delivers inadequate performance. Under Utopian conditions comparable conclusion are achieved which are generally unattainable. Further, model specification becomes increasingly complex and deeply circumstantially dependant -which is in itself not a deterrent but does warrant consideration. In a recent study, these concerns were highlighted and discussed theoretically. We confirm the argument empirically by dissecting the autoencoder's iterative process. In investigating the autoencoder, we see performance degrade as models grow in dimensionality. Visualization of the autoencoder reveals a bias towards the initial randomised topics.