Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation

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Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, methodology): “first order-logic formulas, a system named Logic Tensor Network (LTN) [42] is proposed by extending Neural Tensor Networks (NTN) [47], a stateof-the-art relational embedding method.”
Logic Tensor Networks for Semantic Image Interpretation (2017, background): “LTNs combine learning in deep networks with relational logical constraints [27].”
On a Convex Logic Fragment for Learning and Reasoning (2018, background): “[24], [25] propose a framework called logic tensor networks that integrates learning based on tensor networks [26] with reasoning using first-order Łukasiewicz logic, all implemented in TENSORFLOW.”
Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision (2018, unknown): “Therefore, it is important to test if such a background knowledge improves the performance of a classical Machine Learning setting, see our works [92, 28].”
Logic Tensor Networks (2020, background): “Logic Tensor Network (LTN) [63] is a neurosymbolic formalism and computational model that supports learning and reasoning about data with rich knowledge.”