Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning

We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems. We demonstrate our framework with NeuPSL, a state-of-the-art NeSy architecture. To achieve this, we propose a smooth primal and dual formulation...

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Hauptverfasser:	Dickens, Charles, Gao, Changyu, Pryor, Connor, Wright, Stephen, Getoor, Lise
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning Mathematics - Optimization and Control
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