On Relaxing Determinism in Arithmetic Circuits

August 22, 2017 Β· Declared Dead Β· πŸ› International Conference on Machine Learning

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Authors Arthur Choi, Adnan Darwiche arXiv ID 1708.06846 Category cs.AI: Artificial Intelligence Cross-listed cs.LG Citations 60 Venue International Conference on Machine Learning Last Checked 4 months ago
Abstract
The past decade has seen a significant interest in learning tractable probabilistic representations. Arithmetic circuits (ACs) were among the first proposed tractable representations, with some subsequent representations being instances of ACs with weaker or stronger properties. In this paper, we provide a formal basis under which variants on ACs can be compared, and where the precise roles and semantics of their various properties can be made more transparent. This allows us to place some recent developments on ACs in a clearer perspective and to also derive new results for ACs. This includes an exponential separation between ACs with and without determinism; completeness and incompleteness results; and tractability results (or lack thereof) when computing most probable explanations (MPEs).
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