Exploiting data-independence for fast belief-propagation
Maximum a posteriori (MAP) inference in graphical models requires that we maximize the sum of two terms: a data-dependent term, encoding the conditional likelihood of a certain labeling given an observation, and a data-independent term, encoding some prior on labelings. Often, data-dependent factors contain fewer latent variables than data-independent factors - for instance, many grid and tree-structured models contain only first-order conditionals despite having pairwise priors. In this paper,...[Show more]
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|Source:||Proceedings of International Conference on Machine Learning (ICML 2010)|
|01_Caetano_Exploiting_data-independence_2010.pdf||839.09 kB||Adobe PDF||Request a copy|
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