Title: MIM: Mutual Information Machine. Abstract:     We introduce the Mutual Information Machine (MIM), an autoencoder framework
    for learning joint distributions over observations and latent states. 
    The model formulation reflects two key design principles: 1) symmetry, to encourage 
    the encoder     and decoder to learn different factorizations of the same 
    underlying distribution; and 2) mutual information, to encourage the learning 
    of useful representations for downstream tasks. 
    The objective comprises the Jensen-Shannon divergence between the encoding and 
    decoding joint distributions, plus a mutual information regularizer. 
    We show that this can be bounded by a tractable cross-entropy loss between 
    the true model and a parameterized approximation, and relate this to 
    maximum likelihood estimation and variational autoencoders.
    Experiments show that MIM is capable of learning a latent representation with high mutual information,
    and good unsupervised clustering, while providing NLL comparable to VAE 
    (with a sufficiently expressive architecture).