Relational Neural Markov Random Fields

• URL: http://arxiv.org/abs/2110.09647v1
• Date: Mon, 18 Oct 2021 22:52:54 GMT
• Title: Relational Neural Markov Random Fields
• Authors: Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi
• Abstract summary: We introduce Markov Random Fields (RN-MRFs) which allow handling of complex hybrid domains. We propose a maximum pseudolikelihood estimation-based learning algorithm with importance for training the potential parameters.
• Score: 29.43155380361715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty. However, most of these models have been limited to discrete domains due to their limited potential functions. We introduce Relational Neural Markov Random Fields (RN-MRFs) which allow for handling of complex relational hybrid domains. The key advantage of our model is that it makes minimal data distributional assumptions and can seamlessly allow for human knowledge through potentials or relational rules. We propose a maximum pseudolikelihood estimation-based learning algorithm with importance sampling for training the neural potential parameters. Our empirical evaluations across diverse domains such as image processing and relational object mapping, clearly demonstrate its effectiveness against non-neural counterparts.

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