Multi-Evidence Lifted Message Passing, with Application to PageRank and the Kalman Filter
Multi-Evidence Lifted Message Passing, with Application to PageRank and the Kalman Filter
复制标题
多证据提升消息传递，应用于 PageRank 和卡尔曼滤波器
DOI：
10.1016/j.neucom.2006.11.023
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发表时间：
2007
期刊：
影响因子：
6
通讯作者：
Scott Sanner
中科院分区：
文献类型：
--
作者：
B. Ahmadi;Kristian Kersting;Scott Sanner
提升的消息传递算法利用给定图形模型中的重复结构来高效地回答查询。在给定证据的情况下，他们构建了一个提升的超节点和超势网络，该网络对应于在给定证据的情况下不可区分的节点和势的集合。最近，人们提出了一种有效的算法来更新现有Lift网络的结构，并对证据进行了增量更改。然而，在推理阶段，现有的算法需要为每个证据案例构建一个单独的提升网络，并在每个提升网络上分别运行一个改进的消息传递算法。因此，不会利用推理任务之间的对称性。在本文中，我们提出了一种新的提升消息传递技术，该技术利用了跨多个证据案例的对称性。这种多证据提升推理的好处被展示在几个重要的人工智能任务中，例如通过多证据提升高斯信念传播来计算个性化页面排名和卡尔曼过滤器。
Lifted message passing algorithms exploit repeated structure within a given graphical model to answer queries efficiently. Given evidence, they construct a lifted network of supernodes and superpotentials corresponding to sets of nodes and potentials that are indistinguishable given the evidence. Recently, efficient algorithms were presented for updating the structure of an existing lifted network with incremental changes to the evidence. In the inference stage, however, current algorithms need to construct a separate lifted network for each evidence case and run a modified message passing algorithm on each lifted network separately. Consequently, symmetries across the inference tasks are not exploited. In this paper, we present a novel lifted message passing technique that exploits symmetries across multiple evidence cases. The benefits of this multi-evidence lifted inference are shown for several important AI tasks such as computing personalized PageRanks and Kalman filters via multievidence lifted Gaussian belief propagation.