BriefGPT.xyz
Oct, 2012
从近似到精确——提升后放松、补偿再恢复：Lifted概率推理
Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference
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Guy Van den Broeck, Arthur Choi, Adnan Darwiche
TL;DR
本文提出了一种基于简化的一阶模型的精确提升推理方法，通过松弛一阶约束，补偿松弛并恢复松弛的约束来逐步完善简化的模型，旨在提高命题求解器和提升置信传播的精确度。
Abstract
We propose an approach to
lifted approximate inference
for
first-order probabilistic models
, such as
markov logic networks
. It is based on
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