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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
Event URL: https://openreview.net/forum?id=qey0t9ivuBv »

Deep learning and symbolic reasoning are complementary techniques for an intelligent system. However, principled combinations of these techniques are typically limited in scalability, rendering them ill-suited for real-world applications. We propose Scallop, a system that builds upon probabilistic deductive databases, to bridge this gap. On synthetic tasks involving mathematical and logical reasoning, Scallop scales significantly better without sacrificing accuracy compared to DeepProbLog, a principled neural logic programming approach. Scallop also scales to a real-world Visual Question Answering (VQA) benchmark that requires multi-hop reasoning, achieving 84.22% accuracy and outperforming two VQA-tailored models based on Neural Module Networks and transformers 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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