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DTSTART;TZID=America/New_York:20221130T120000
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DTSTAMP:20260405T082033
CREATED:20220909T155650Z
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UID:7305-1669809600-1669815000@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: Scallop: A Language for Neuro-Symbolic Programming\, Mayur Naik (University of Pennsylvania)
DESCRIPTION:ABSTRACT: \nNeurosymbolic 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. \nThis is joint work with PhD students Ziyang Li and Jiani Huang.
URL:https://seasevents.nmsdev7.com/event/asset-seminar-tba-mayur-naik-university-of-pennsylvania/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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