Deep Bucket Elimination
Deep Bucket Elimination
Yasaman Razeghi, Kalev Kask, Yadong Lu, Pierre Baldi, Sakshi Agarwal, Rina Dechter
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 4235-4242.
https://doi.org/10.24963/ijcai.2021/582
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.
Keywords:
Uncertainty in AI: Approximate Probabilistic Inference
Uncertainty in AI: Exact Probabilistic Inference