Quiet: Faster Belief Propagation for Images and Related Applications
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 3497-3502.
Abstract
Belief propagation over Markov random fields has been successfully used in many AI applications since it yields accurate inference results by iteratively updating messages between nodes. However, its high computation costs are a barrier to practical use. This paper presents an efficient approach to belief propagation. Our approach, Quiet, dynamically detects converged messages to skip unnecessary updates in each iteration while it theoretically guarantees to output the same results as the standard approach used to implement belief propagation. Experiments show that our approach is significantly faster than existing approaches without sacrificing inference quality.
@inproceedings{ijcai2015p492,
title = {Quiet: Faster Belief Propagation for Images and Related Applications},
author = {Fujiwara, Yasuhiro and Shasha, Dennis E.},
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 = {3497--3502},
year = {2015},
month = {7},
note = {Special Track on Machine Learning},
url = {https://ijcai.org/proceedings/2015/492},
}
TY - CPAPER TI - Quiet: Faster Belief Propagation for Images and Related Applications AU - Fujiwara, Yasuhiro AU - Shasha, Dennis E. T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 3497 EP - 3502 UR - https://ijcai.org/proceedings/2015/492 ER -Copied
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