TL;DR
A novel and interpretable approach to perform symbolic integration using deep learning through integral rule prediction to speed up the search and introduces the first-of-its-kind symbolic integration rules dataset comprising two million distinct functions and integration rule pairs.
Recent advancements in neuro-symbolic learning (NeSy) have shown significant promise in integrating deep learning with symbolic reasoning, offering both interpretability and generalization. However, the prevalence of reasoning shortcuts, where the NeSy system predicts incorrect intermediate concepts while maintaining h...
A systematic review and synthesis of symbolic explainable AI (XAI) for deep learning is provided and a conceptual framework is proposed that clarifies training–inference flows, explanation interfaces, human feedback, and governance touchpoints is proposed.
This overview addresses this issue by providing a gentle introduction to RSs, discussing their causes and consequences in intuitive terms, and details methods for dealing with RSs, including mitigation and awareness strategies, and maps their benefits and limitations.
This paper proposes combining a Constraint Programming model with a Masked Language Model ( mlm) to perform Large Neighbourhood Search ( lns), and shows that it can quickly generate many high-quality sentences and molecules, even for highly-constrained tasks.
SymBOL accurately recovers governing equations and provides interpretable pathways for equation discovery when applied to real-world systems in materials science and epidemiology, and underscores the potential of SymBOL for advancing scientific discovery.
Although performance of language-based models is improved by scaling, whether the gap to a structure-aware architecture can eventually be eliminated remains untested.
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