Greedy Structure Search for Sum-Product Networks
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 932-937.
Abstract
Sum-product networks (SPNs) are rooted, directed acyclic graphs (DAGs) of sum and product nodes with well-defined probabilistic semantics. Moreover, exact inference in the distribution represented by an SPN is guaranteed to take linear time in the size of the DAG. In this paper we introduce an algorithm that learns the structure of an SPN using a greedy search approach. It incorporates methods used in a previous SPN structure-learning algorithm, but, unlike the previous algorithm, is not limited to learning tree-structured SPNs. Several proven ideas from circuit complexity theory along with our experimental results provide evidence for the advantages of SPNs with less-restrictive, non-tree structures.
@inproceedings{ijcai2015p136,
title = {Greedy Structure Search for Sum-Product Networks},
author = {Dennis, Aaron W. and Ventura, Dan},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {932--937},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/136},
}
TY - CPAPER TI - Greedy Structure Search for Sum-Product Networks AU - Dennis, Aaron W. AU - Ventura, Dan T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 932 EP - 937 UR - https://ijcai.org/proceedings/2015/136 ER -Copied
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