no code implementations • 12 May 2024 • Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van Den Broeck
We discuss the semantic loss, which injects knowledge about such structure, defined symbolically, into training by minimizing the network's violation of such dependencies, steering the network towards predicting distributions satisfying the underlying structure.
no code implementations • 7 Feb 2024 • Paolo Morettin, Andrea Passerini, Roberto Sebastiani
The probabilistic formal verification (PFV) of AI systems is in its infancy.
no code implementations • 7 Jun 2023 • Vincent Derkinderen, Pedro Zuidberg Dos Martires, Samuel Kolb, Paolo Morettin
Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF).
no code implementations • 13 Feb 2023 • Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani
The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research.
1 code implementation • 28 Jun 2022 • Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani
Weighted Model Integration (WMI) is a popular formalism aimed at unifying approaches for probabilistic inference in hybrid domains, involving logical and algebraic constraints.
no code implementations • NeurIPS 2020 • Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van Den Broeck
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.
1 code implementation • NeurIPS 2020 • Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini
Generative Adversarial Networks (GANs) struggle to generate structured objects like molecules and game maps.
1 code implementation • ICML 2020 • Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van Den Broeck
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.
no code implementations • 20 Sep 2019 • Zhe Zeng, Fanqi Yan, Paolo Morettin, Antonio Vergari, Guy Van Den Broeck
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.