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Title: NeuroBE: Escalating neural network approximations of Bucket Elimination
A major limiting factor in graphical model inference is the complexity of computing the partition function. Exact message-passing algorithms such as Bucket Elimination (BE) require exponential memory to compute the partition function; therefore, approximations are necessary. In this paper, we build upon a recently introduced methodology called Deep Bucket Elimination (DBE) that uses classical Neural Networks to approximate messages generated by BE for large buckets. The main feature of our new scheme, renamed NeuroBE, is that it customizes the architecture of the neural networks, their learning process and in particular, adapts the loss function to the internal form or distribution of messages. Our experiments demonstrate significant improvements in accuracy and time compared with the earlier DBE scheme.  more » « less
Award ID(s):
2008516
NSF-PAR ID:
10376089
Author(s) / Creator(s):
; ; ;
Editor(s):
Cussens, James; Zhang, Kun
Date Published:
Journal Name:
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PLMR
Volume:
180
Page Range / eLocation ID:
11-21
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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