2,838 research outputs found
Deep Exponential Families
We describe \textit{deep exponential families} (DEFs), a class of latent
variable models that are inspired by the hidden structures used in deep neural
networks. DEFs capture a hierarchy of dependencies between latent variables,
and are easily generalized to many settings through exponential families. We
perform inference using recent "black box" variational inference techniques. We
then evaluate various DEFs on text and combine multiple DEFs into a model for
pairwise recommendation data. In an extensive study, we show that going beyond
one layer improves predictions for DEFs. We demonstrate that DEFs find
interesting exploratory structure in large data sets, and give better
predictive performance than state-of-the-art models
Modeling heterogeneity in random graphs through latent space models: a selective review
We present a selective review on probabilistic modeling of heterogeneity in
random graphs. We focus on latent space models and more particularly on
stochastic block models and their extensions that have undergone major
developments in the last five years
A semiparametric extension of the stochastic block model for longitudinal networks
To model recurrent interaction events in continuous time, an extension of the
stochastic block model is proposed where every individual belongs to a latent
group and interactions between two individuals follow a conditional
inhomogeneous Poisson process with intensity driven by the individuals' latent
groups. The model is shown to be identifiable and its estimation is based on a
semiparametric variational expectation-maximization algorithm. Two versions of
the method are developed, using either a nonparametric histogram approach (with
an adaptive choice of the partition size) or kernel intensity estimators. The
number of latent groups can be selected by an integrated classification
likelihood criterion. Finally, we demonstrate the performance of our procedure
on synthetic experiments, analyse two datasets to illustrate the utility of our
approach and comment on competing methods
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