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OverviewProbabilistic inference in graphical models is a key task in many applications, from machine vision to computational biology. Since the problem is generally computationally intractable many approximations have been suggested over the years.The goal of this challenge is to evaluate inference algorithms on difficult large scale problems. Algorithms for MAP, marginals, and partition function approximation will be evaluated. An online leaderboard will show the relative rankings of the algorithms. Challenge ResultsThe challenge is now over. The winners are:On the marginals and partition function tasks (20 seconds, 20 minutes and 1 hour): Vibhav Gogate, University of Texas, Dallas. On the MAP task (20 seconds, 20 minutes and 1 hour): Lars Otten, Alexander Ihler, Kalev Kask, Rina Dechter, UC Irvine. Also using some open-source code by Frank Hutter, UBC. Congratulations to the two winning teams! This was a very close race, as you can see from the leaderboard . NewsThe protein-protein networks have been released (see Examples). Provisional Timetable
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AcknowledgmentsThe organizers gratefully acknowledge support from the PASCAL Network of Excellence. |
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