IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirements
arxiv(2024)
摘要
Neurosymbolic background knowledge and the expressivity required of its logic
can break Machine Learning assumptions about data Independence and Identical
Distribution. In this position paper we propose to analyze IID relaxation in a
hierarchy of logics that fit different use case requirements. We discuss the
benefits of exploiting known data dependencies and distribution constraints for
Neurosymbolic use cases and argue that the expressivity required for this
knowledge has implications for the design of underlying ML routines. This opens
a new research agenda with general questions about Neurosymbolic background
knowledge and the expressivity required of its logic.
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