Fast and Accurate Prediction via Evidence-Specific MRF Structure
نویسندگان
چکیده
We are interested in speeding up approximate inference in Markov Random Fields (MRFs). We present a new method that uses gates—binary random variables that determine which factors of the MRF to use. Which gates are open depends on the observed evidence; when many gates are closed, the MRF takes on a sparser and faster structure that omits “unnecessary” factors. We train parameters that control the gates, jointly with the ordinary MRF parameters, in order to locally minimize an objective that combines loss and runtime.
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