6 papers
Generate in the Chart, Not on the Boundary: Function-Symbol Grounding for Hard Constraints in LTN-GANs
Nijesh Upreti, Vaishak Belle
Logic Tensor Network-Enhanced Generative Adversarial Networks (LTN-GANs) inject background knowledge by grounding each logical axiom as a predicate and training the generator to ra…
Neuro-symbolic Weak Supervision: Theory and Semantics
Nijesh Upreti, Vaishak Belle
Weak supervision enables machine learning models to learn from limited or noisy labels, but it introduces challenges in reliability and semantic clarity, particularly in multi-inst…
Logic Tensor Network-Enhanced Generative Adversarial Network
Nijesh Upreti, Vaishak Belle
In this paper, we introduce Logic Tensor Network-Enhanced Generative Adversarial Network (LTN-GAN), a novel framework that enhances Generative Adversarial Networks (GANs) by incorp…
Satisfiability Modulo Theory Meets Inductive Logic Programming
Nijesh Upreti, Vaishak Belle
Inductive Logic Programming (ILP) provides interpretable rule learning in relational domains, yet remains limited in its ability to induce and reason with numerical constraints. Cl…
Towards Developing Ethical Reasoners: Integrating Probabilistic Reasoning and Decision-Making for Complex AI Systems
Nijesh Upreti, Jessica Ciupa, Vaishak Belle
A computational ethics framework is essential for AI and autonomous systems operating in complex, real-world environments. Existing approaches often lack the adaptability needed to…
An Algebraic Framework for Hierarchical Probabilistic Abstraction
Nijesh Upreti, Vaishak Belle
Abstraction is essential for reducing the complexity of systems across diverse fields, yet designing effective abstraction methodology for probabilistic models is inherently challe…