TL;DR
DeepLog is a versatile neurosymbolic framework integrated with PyTorch that compiles various logical specifications into optimized circuits, facilitating research and development in neurosymbolic AI.
Contribution
It introduces a universal backend that unifies diverse neurosymbolic systems, enabling easy prototyping and high-performance execution within standard deep learning workflows.
Findings
DeepLog can emulate multiple neurosymbolic paradigms.
It compiles high-level specifications into efficient arithmetic circuits.
The framework lowers barriers for neurosymbolic AI development.
Abstract
DeepLog is an operational neurosymbolic framework that unifies logic and deep learning within standard PyTorch workflows. While existing neurosymbolic systems focus on a particular paradigm and semantics, DeepLog serves as a universal backend that can emulate many systems in the neurosymbolic alphabet soup. By treating diverse neurosymbolic languages as high-level specifications, the DeepLog software automatically compiles them into optimized arithmetic circuits. This design lowers the barrier for machine learning practitioners by treating logic as composable modules, while providing neurosymbolic developers with a shared, high-performance basis for prototyping new integration strategies. The code is available here: https://github.com/ML-KULeuven/deeplog
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