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Found 8 papers, 3 papers with code
A Unified Framework for Probabilistic Verification of AI Systems via Weighted Model Integration
The probabilistic formal verification (PFV) of AI systems is in its infancy.
Top-Down Knowledge Compilation for Counting Modulo Theories
Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF).
Enhancing SMT-based Weighted Model Integration by Structure Awareness
The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research.
SMT-based Weighted Model Integration with Structure Awareness
Weighted Model Integration (WMI) is a popular formalism aimed at unifying approaches for probabilistic inference in hybrid domains, involving logical and algebraic constraints.
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
Weighted model integration (WMI) is a framework to perform advanced probabilistic inference on hybrid domains, i. e., on distributions over mixed continuous-discrete random variables and in presence of complex logical and arithmetic constraints.
Efficient Generation of Structured Objects with Constrained Adversarial Networks
Generative Adversarial Networks (GANs) struggle to generate structured objects like molecules and game maps.
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Weighted model integration (WMI) is a very appealing framework for probabilistic inference: it allows to express the complex dependencies of real-world problems where variables are both continuous and discrete, via the language of Satisfiability Modulo Theories (SMT), as well as to compute probabilistic queries with complex logical and arithmetic constraints.
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing
Weighted model integration (WMI) is a very appealing framework for probabilistic inference: it allows to express the complex dependencies of real-world hybrid scenarios where variables are heterogeneous in nature (both continuous and discrete) via the language of Satisfiability Modulo Theories (SMT); as well as computing probabilistic queries with arbitrarily complex logical constraints.
