[Lisa_teatalk] (no subject)

Jörg Bornschein bornj at iro.umontreal.ca
Thu Nov 12 11:44:56 EST 2015


Hi,


I would like to announce my own tea-talk for tomorrow -- starting at 2:30pm
in AA3195. I will present some joint work with Asja and Samira:



Time: 2:30pm - 3:30pm

Where: AA3195

Title: Training deep generative (bidirectional) Helmholtz-Machines

== Abstract ==


Unsupervised training of deep generative models containing latent variables
and performing inference remains a challenging problem. Various methods
have been proposed and many of them train an auxiliary model to perform
approximate inference for the generative model which is fitted to the
training data. The top-down generative model is typically a directed model
that starts from some prior over latent variables at the top, down to a
distribution over the observed variables at the bottom. The approximate
inference model runs in the opposite direction and is typically trained to
efficiently infer high probability latent states given some observed data.
After presenting some of the well-known / state-of-the-art approaches (Wake
sleep, NVIL and VAE) I will to talk about a new method we have been
investigating, called a bidirectional Helmholtz machine (BiHM), that is
based on the idea that the generative model should be close to the class of
distributions that can be modeled by our approximate inference
distribution. We achieve this by interpreting both the top-down and the
bottom-up directed models as approximate inference distributions and by
defining the target distribution we fit to the training data to be the
geometric mean of these two. We present an upper-bound for the
log-likelihood of this model and we show that optimizing this bound will
pressure the model to stay close to the approximate inference distributions.



Hope to see you tomorrow!


j
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