Learning symbolic rules for reasoning

Jia Deng / Princeton

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Abstract: Symbolic reasoning, rule-based symbol manipulation, is a hallmark of human intelligence. However, rule-based systems have had limited success competing with learning-based systems outside formalized domains such as automated theorem proving. One hypothesis is that this is due to the manual construction of rules in past attempts. In this talk, I will present a method for automatic learning rules from data. This approach can express both formal logic and natural language sentences, and can induce rules from training data consisting of questions and answers, with or without intermediate reasoning steps. This approach performs well on multiple reasoning benchmarks; it learns compact models with much less data and produces not only answers but also checkable proofs.

Bio: Jia Deng is an Assistant Professor of Computer Science at Princeton University. His research focus is on computer vision and machine learning. He received his Ph.D. from Princeton University and his B.Eng. from Tsinghua University, both in computer science. He is a recipient of the Sloan Research Fellowship, the NSF CAREER award, the ONR Young Investigator award, an ICCV Marr Prize, and two ECCV Best Paper Awards.