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Title: Deep Bucket Elimination

Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over probabilistic and deterministic graphical models exactly.However, it often requires exponentially high levels of memory (in the induced-width) preventing its execution. In the spirit of exploiting Deep Learning for inference tasks, in this paper, we will use neural networks to approximate BE.The resulting Deep Bucket Elimination (DBE) algorithm is developed for computing the partition function.We provide a proof-of-concept empirically using instances from several different benchmarks, showing that DBE can be a more accurate approximation than current state-of-the-art approaches for approximating BE (e.g. the mini-bucket schemes), especially when problems are sufficiently hard.

 
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Award ID(s):
2008516
NSF-PAR ID:
10293539
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
International Joint Conference of Artificial Intelligence {ijcai}
Page Range / eLocation ID:
4235 to 4242
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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