  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.
