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Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning
Jiani Huang · Ziyang Li · Binghong Chen · Karan Samel · Mayur Naik · Le Song · Xujie Si

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ None #None

Deep learning and symbolic reasoning are complementary techniques for an intelligent system. However, principled combinations of these techniques have limited scalability, rendering them ill-suited for real-world applications. We propose Scallop, a system that builds upon probabilistic deductive databases, to bridge this gap. The key insight underlying Scallop is a provenance framework that introduces a tunable parameter to specify the level of reasoning granularity. Scallop thereby i) generalizes exact probabilistic reasoning, ii) asymptotically reduces computational cost, and iii) provides relative accuracy guarantees. On a suite of tasks that involve mathematical and logical reasoning, Scallop scales significantly better without sacrificing accuracy compared to DeepProbLog, a principled neural logic programming approach. We also create and evaluate on a real-world Visual Question Answering (VQA) benchmark that requires multi-hop reasoning. Scallop outperforms two VQA-tailored models, a Neural Module Networks based and a transformer based model, by 12.42% and 21.66% respectively.

Author Information

Jiani Huang (University of Pennsylvania)
Ziyang Li (University of Pennsylvania)
Binghong Chen (Georgia Institute of Technology)
Karan Samel (Georgia Institute of Technology)
Mayur Naik (University of Pennsylvania)
Le Song (Georgia Institute of Technology)
Xujie Si (University of Pennsylvania)

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