aiqbench analysis
This paper presents Neural-Symbolic Energy-Based Models (NeSy-EBMs), a unified mathematical framework designed to streamline the integration of neural and symbolic reasoning methods. By deriving general gradient expressions and introducing four novel learning techniques, the authors provide a scalable open-source library that enhances expressivity across tasks like image classification and autonomous driving. The work addresses a critical gap in organizing diverse modeling patterns, offering researchers a robust foundation for next-generation hybrid AI systems.
A Mathematical Framework and a Suite of Learning Techniques for Neural-Symbolic Systems
arXiv:2407.09693v3 Announce Type: replace Abstract: The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and symbolic methods. However, a unifying framework is needed to organize common NeSy modeling patterns and develop general learning approaches. In this paper, we introduce Neural-Symbolic Energy-Based Models (NeSy-EBMs), a unifying mathematical framework for discriminative and generative NeSy modeling. Importantly, NeSy-EBMs allow the derivation of general expressions for gradients of prominent learning losses, and we introduce a suite of four learning approaches that leverage methods from multiple domains, including bilevel and stochastic policy optimization. Finally, we ground the NeSy-EBM framework with Neural Probabilistic Soft Logic (NeuPSL), an open-source NeSy-EBM library designed for scalability and expressivity, facilitating the real-world application of NeSy systems. Through extensive empirical analysis across multiple datasets, we study NeSy-EBMs as instantiated through NeuPSL in tasks including image classification, graph node labeling, autonomous vehicle situation awareness, and question answering. The results provide evidence for the practical utility of this instantiation in the studied settings.
Abstract:The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and symbolic methods. However, a unifying framework is needed to organize common NeSy modeling patterns and develop general learning approaches. In this paper, we introduce Neural-Symbolic Energy-Based Models (NeSy-EBMs), a unifying mathematical framework for discriminative and generative NeSy modeling. Importantly, NeSy-EBMs allow the derivation of general expressions for gradients of prominent learning losses, and we introduce a suite of four learning approaches that leverage methods from multiple domains, including bilevel and stochastic policy optimization. Finally, we ground the NeSy-EBM framework with Neural Probabilistic Soft Logic (NeuPSL), an open-source NeSy-EBM library designed for scalability and expressivity, facilitating the real-world application of NeSy systems. Through extensive empirical analysis across multiple datasets, we study NeSy-EBMs as instantiated through NeuPSL in tasks including image classification, graph node labeling, autonomous vehicle situation awareness, and question answering. The results provide evidence for the practical utility of this instantiation in the studied settings.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2407.09693 [cs.LG]
(or arXiv:2407.09693v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2407.09693 Focus to learn more arXiv-issued DOI via DataCite
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