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Deep learning and logical reasoning are integrated through research initiatives like SATNet [1] and the development of Logic Tensor Networks [2], [3], which aim to combine data-driven learning with symbolic knowledge.

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Neuro-symbolic AI - Wikipedia en.wikipedia.org Wikipedia 2 facts
referenceLuciano Serafini and Artur d'Avila Garcez authored the 2016 paper 'Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge', published on arXiv.
referenceLuciano Serafini and Artur d'Avila Garcez authored 'Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge', which discusses integrating deep learning with logical reasoning.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org arXiv 1 fact
referenceSATNet, developed by Po-Wei Wang, Priya Donti, Bryan Wilder, and Zico Kolter in 2019, is a differentiable satisfiability solver designed to bridge deep learning and logical reasoning.