4 code implementations • XVIIth International Conference of the Italian Association for Artificial Intelligence 2018 • Teresa M.A. Basile, Nicola Di Mauro, Floriana Esposito
Promising approaches for MLC are those able to capture label dependencies by learning a single probabilistic model—differently from other competitive approaches requiring to learn many models.
no code implementations • 9 Oct 2017 • Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting
While all kinds of mixed data -from personal data, over panel and scientific data, to public and commercial data- are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult.
no code implementations • 29 Aug 2016 • Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Sum-Product Networks (SPNs) are recently introduced deep tractable probabilistic models by which several kinds of inference queries can be answered exactly and in a tractable time.
no code implementations • 8 Aug 2016 • Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Probabilistic models learned as density estimators can be exploited in representation learning beside being toolboxes used to answer inference queries only.
no code implementations • 15 Nov 2013 • Nicola Di Mauro, Floriana Esposito
Many statistical relational learning approaches try to deal with these problems by combining the construction of relevant relational features with a probabilistic tool.