Keywords: Neural Symoblic, Probabilistic Graphical Models
TL;DR: In this work, we examine the learning process for Neural Probabilistic Soft Logic (NeuPSL).
Abstract: In this work, we examine the learning process for Neural Probabilistic Soft Logic (NeuPSL). NeuPSL is a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks to create a tractable probabilistic model that supports end-to-end learning via back-propagation. We investigate two common learning losses, Energy-based and Structured Perceptron. We provide formal definitions, and identify and propose principled fixes to degenerate solutions. We then perform an extensive evaluation over a canonical NeSy task
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