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Abstract
Continuous relaxations play an important role in discrete optimization, but have not seen much use in approximate probabilistic inference. Here we show that a general form of the Gaussian Integral Trick makes it possible to transform a wide class of discrete variable undirected models into fully continuous systems. The continuous representation allows the use of gradientbased Hamiltonian Monte Carlo for inference, results in new ways of estimating normalization constants (partition functions), and in general opens up a number of new avenues for inference in difficult discrete systems. We demonstrate some of these continuous relaxation inference algorithms on a number of illustrative problems.
Original language  English 

Title of host publication  Advances in Neural Information Processing Systems 25 
Subtitle of host publication  26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 36, 2012, Lake Tahoe, Nevada, United States 
Publisher  MIT Press 
Pages  32033211 
Number of pages  9 
Publication status  Published  2012 
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Dive into the research topics of 'Continuous Relaxations for Discrete Hamiltonian Monte Carlo'. Together they form a unique fingerprint.Projects
 1 Finished

Fast, locally adaptive interference for machine learning in graphical models
1/10/11 → 30/09/14
Project: Research