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Found 2 papers, 1 papers with code
Parallel Neurosymbolic Integration with Concordia
Parallel neurosymbolic architectures have been applied effectively in NLP by distilling knowledge from a logic theory into a deep model. However, prior art faces several limitations including supporting restricted forms of logic theories and relying on the assumption of independence between the logic and the deep network.
Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models
We present the first principled approach for mining structural motifs in lifted graphical models, languages that blend first-order logic with probabilistic models, which uses a stochastic process to measure the similarity of entities in the data.
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