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

翻译：DeepLog是一个可操作的神经符号框架，能在标准PyTorch工作流中统一逻辑与深度学习。现有神经符号系统聚焦于特定范式和语义，而DeepLog作为通用后端，可模拟神经符号领域众多系统。通过将多样化的神经符号语言视为高级规范，DeepLog软件自动将其编译为优化的算术电路。这种设计将逻辑视为可组合模块，降低了机器学习从业者的使用门槛，同时为神经符号开发者提供了用于原型化新型集成策略的共享高性能基础。代码详见：https://github.com/ML-KULeuven/deeplog