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- W4387209110 abstract "Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints. This paper proposes a novel neural layer, LogicMP, whose layers perform mean-field variational inference over an MLN. It can be plugged into any off-the-shelf neural network to encode FOLCs while retaining modularity and efficiency. By exploiting the structure and symmetries in MLNs, we theoretically demonstrate that our well-designed, efficient mean-field iterations effectively mitigate the difficulty of MLN inference, reducing the inference from sequential calculation to a series of parallel tensor operations. Empirical results in three kinds of tasks over graphs, images, and text show that LogicMP outperforms advanced competitors in both performance and efficiency." @default.
- W4387209110 created "2023-09-30" @default.
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- W4387209110 date "2023-09-27" @default.
- W4387209110 modified "2023-10-01" @default.
- W4387209110 title "LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints" @default.
- W4387209110 doi "https://doi.org/10.48550/arxiv.2309.15458" @default.
- W4387209110 hasPublicationYear "2023" @default.
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