December 13th, 2012 @ 2:00pm Justin Domke (RIT)

Single-Loop Surrogate Likelihood Training of Graphical Models

This talk will describe a faster “single-loop” method for training Conditional Random Fields (CRFs). CRFs are used to model problems in computer vision, natural language processing and computational biology. In general, exact learning and inference in such models is intractable. As such, when learning CRFs, it is common to use approximate message-passing inference algorithms to estimate gradients of a “surrogate likelihood” objective. However, this is a rather slow double-loop optimization, where message-passing must be iterated to convergence to estimate each likelihood gradient. I will show how, with tree-reweighted belief propagation, one may reformulate the learning objective into an equivalent single-loop optimization, where inference and learning are solved simultaneously. Experiments on computer vision and multi-label prediction suggest that this approach is faster and more reliable than double-loop learning.

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