14 citations · 28 across the 12 of their papers we have counts for
7 papers · 1 filter
Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
Emanuele Marconato, Samuele Bortolotti, Emile van Krieken +6
Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.g. safety or structural constraints. As such, it represents…
Probabilistic ML Verification via Weighted Model Integration
Paolo Morettin, Andrea Passerini, Roberto Sebastiani
In machine learning (ML) verification, the majority of procedures are non-quantitative and therefore cannot be used for verifying probabilistic models, or be applied in domains whe…
Top-Down Knowledge Compilation for Counting Modulo Theories
Vincent Derkinderen, Pedro Zuidberg Dos Martires, Samuel Kolb +1
Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF). Translating an arbitrary formul…
Enhancing SMT-based Weighted Model Integration by Structure Awareness
Giuseppe Spallitta, Gabriele Masina, Paolo Morettin +2
The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research. Whereas substantial progress…
SMT-based Weighted Model Integration with Structure Awareness
Giuseppe Spallitta, Gabriele Masina, Paolo Morettin +2
Weighted Model Integration (WMI) is a popular formalism aimed at unifying approaches for probabilistic inference in hybrid domains, involving logical and algebraic constraints. Des…
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Zhe Zeng, Paolo Morettin, Fanqi Yan +2
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…
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing
Zhe Zeng, Fanqi Yan, Paolo Morettin +2
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 varia…