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Creators/Authors contains: "Agarwal, Sakshi"

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  1. ; ; ; (, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PLMR)
    Cussens, James; Zhang, Kun (Ed.)
    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. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; (, International Joint Conference of Artificial Intelligence {ijcai})
    null (Ed.)
    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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    Full Text Available