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AIC Seminar Series

Amortized inference for structured output prediction

Vivek SrikumarStanford University[Home Page]

Notice:  Hosted by Rodrigo Braz

Date:  2013-11-07 at 16:30

Location:  EK255 (SRI E building)  (Directions)

   Abstract

Structured output prediction in NLP consists of using a learned model with an inference algorithm to predict structures for inputs. Typically, at prediction time, each example is considered to be independent of all other examples.

In this talk, I will argue that we can take advantage of regularities of outputs over entire datasets to give an amortized improvement in inference time over the lifetime of the predictor. I will present different amortized inference algorithms, which can re-use computation from previously solved instances to speed up any standard inference algorithm and retain their exactness properties. Then, I will discuss an approach for decomposed amortized inference that allows us to apply this idea for sub-parts of the output structure. Through the talk, I will present results which show that, for the tasks of semantic role labeling and entity-relation extraction, the various amortized inference algorithms give significant decrease in the number of calls to the underlying inference engine.

   Bio for Vivek Srikumar

I am a post-doc at Stanford University with Chris Manning and the Stanford NLP group. I obtained my Ph.D. from the Computer Science Department of the University of Illinois at Urbana-Champaign, where I worked with Dan Roth and the Cognitive Computation group.

My research deals with Machine Learning and Natural Language Processing.

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