Recent advances in deep learning have brought to the fore models that can
make multiple computational steps in the service of completing a task; these
are capable of describ- ing long-term dependencies in sequential data. Novel
recurrent attention models over possibly large external memory modules
constitute the core mechanisms that enable these capabilities. Our work
addresses learning subtler and more complex underlying temporal dynamics in
language modeling tasks that deal with sparse sequential data. To this end, we
improve upon these recent advances, by adopting concepts from the field of
Bayesian statistics, namely variational inference. Our proposed approach
consists in treating the network parameters as latent variables with a prior
distribution imposed over them. Our statistical assumptions go beyond the
standard practice of postulating Gaussian priors. Indeed, to allow for handling
outliers, which are prevalent in long observed sequences of multivariate data,
multivariate t-exponential distributions are imposed. On this basis, we proceed
to infer corresponding posteriors; these can be used for inference and
prediction at test time, in a way that accounts for the uncertainty in the
available sparse training data. Specifically, to allow for our approach to best
exploit the merits of the t-exponential family, our method considers a new
t-divergence measure, which generalizes the concept of the Kullback-Leibler
divergence. We perform an extensive experimental evaluation of our approach,
using challenging language modeling benchmarks, and illustrate its superiority
over existing state-of-the-art techniques