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We introduce Deep Adaptive Semantic Logic (DASL), a novel framework forautomating the generation of deep neural networks that incorporatesuser-provided formal knowledge to improve learning from data. We provide formalsemantics that demonstrate that our knowledge representation captures all offirst order logic and that finite sampling from infinite domains converges tocorrect truth values. DASL's representation improves on prior neural-symbolicwork by avoiding vanishing gradients, allowing deeper logical structure, andenabling richer interactions between the knowledge and learning components. Weillustrate DASL through a toy problem in which we add structure to an imageclassification problem and demonstrate that knowledge of that structure reducesdata requirements by a factor of $1000$. We then evaluate DASL on a visualrelationship detection task and demonstrate that the addition of commonsenseknowledge improves performance by $10.7\%$ in a data scarce setting.

Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks