Zobrazeno 1 - 10
of 463
pro vyhledávání: '"Esposito, Floriana"'
Autor:
Molina, Alejandro, Vergari, Antonio, Di Mauro, Nicola, Natarajan, Sriraam, Esposito, Floriana, Kersting, Kristian
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. Users spend significa
Externí odkaz:
http://arxiv.org/abs/1710.03297
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. Up to now, they have been largely used as black box density estimator
Externí odkaz:
http://arxiv.org/abs/1608.08266
Probabilistic models learned as density estimators can be exploited in representation learning beside being toolboxes used to answer inference queries only. However, how to extract useful representations highly depends on the particular model involve
Externí odkaz:
http://arxiv.org/abs/1608.02341
Autor:
Di Mauro, Nicola, Esposito, Floriana
Dealing with structured data needs the use of expressive representation formalisms that, however, puts the problem to deal with the computational complexity of the machine learning process. Furthermore, real world domains require tools able to manage
Externí odkaz:
http://arxiv.org/abs/1311.3735
The probabilistic graphs framework models the uncertainty inherent in real-world domains by means of probabilistic edges whose value quantifies the likelihood of the edge existence or the strength of the link it represents. The goal of this paper is
Externí odkaz:
http://arxiv.org/abs/1205.5367
In Artificial Intelligence with Coalition Structure Generation (CSG) one refers to those cooperative complex problems that require to find an optimal partition, maximising a social welfare, of a set of entities involved in a system into exhaustive an
Externí odkaz:
http://arxiv.org/abs/1103.1157
We tackle the problem of multi-class relational sequence learning using relevant patterns discovered from a set of labelled sequences. To deal with this problem, firstly each relational sequence is mapped into a feature vector using the result of a f
Externí odkaz:
http://arxiv.org/abs/1006.5188