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ASSET Seminar: Scallop: A Language for Neuro-Symbolic Programming, Mayur Naik (University of Pennsylvania)
November 30, 2022 at 12:00 PM - 1:30 PM
Neurosymbolic learning is an emerging paradigm which, at its core, combines the otherwise complementary worlds of classical algorithms and deep learning; in doing so, it ushers in more accurate, interpretable, and domain-aware solutions for today’s most complex machine learning challenges. I will begin by reviewing the various fundamentals, such as algorithmic supervision, symbolic reasoning, and differentiable programming, which have defined this intersection thus far. I will then present Scallop, a general-purpose programming language that allows for a wide range of modern neurosymbolic learning applications to be written and trained in a data and compute efficient manner. Scallop is able to achieve these goals through three salient overarching design decisions: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that builds on Datalog; and 3) a framework for automatic and efficient differentiable reasoning that is based on the theory of provenance semirings. I will present case studies demonstrating how Scallop expresses algorithmic reasoning in a diverse and challenging set of AI tasks, provides a succinct interface for machine learning programmers to integrate logical domain-specific knowledge, and outperforms state-of-the-art deep neural network models in terms of accuracy and efficiency.
This is joint work with PhD students Ziyang Li and Jiani Huang.
Mayur Naik, Ph.D.
University of Pennsylvania
Mayur Naik is a professor in the department of Computer and Information Science at the University of Pennsylvania. He is broadly interested in topics related to programming languages and artificial intelligence. His current research is motivated by the need to make AI applications safe, interpretable, data-efficient, and easier to develop. To this end, his research group is developing principled yet practical approaches to neuro-symbolic programming, and applying them in high-stakes domains like healthcare and robotics. He obtained his Ph.D. in Computer Science from Stanford University, and was a research scientist at Intel Labs Berkeley and a faculty at Georgia Tech.