

























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: this https URL
| Comments: | Preprint accepted at IJCAI2026 Demo Track |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.10279 [cs.LG] |
| (or arXiv:2605.10279v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10279 arXiv-issued DOI via DataCite (pending registration) |
From: Stefano Colamonaco [view email]
[v1]
Mon, 11 May 2026 09:39:59 UTC (92 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。