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Pierre Bruno Baque
presents
Multi-Modal Mean-Fields via Cardinality-Based Clamping
 
On 2016-12-20 11:00 at G205
 
Mean Field inference is central to statistical physics.  It has attracted much
interest in the Computer Vision community to efficiently solve problems
expressible in terms of large Conditional Random Fields. However, since it
models the posterior probability distribution as a product of marginal
probabilities, it may fail to properly account for important dependencies
between variables. We therefore replace the fully  factorized distribution of
Mean Field by  a weighted  mixture of  such  distributions,  that similarly
minimizes the KL-Divergence to  the true posterior. By introducing two new
ideas, namely, conditioning  on groups  of  variables instead of single 
ones and using  a parameter of the conditional random field  potentials, that
we identify  to the temperature in the sense of statistical  physics to select
such groups, we can perform this minimization  efficiently.  Our extension of
the clamping method proposed in previous works allows us to both produce a more
descriptive approximation of the true posterior and, inspired by the diverse
MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that
this positively impacts real-world algorithms that initially relied on mean
fields.

Pierre Bruno Baque is PhD student at the Computer Vision Lab, EPFL Lausanne.
Currently he his visiting Honeywell, Prague under EU FP7 project CENTAUR.
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