Zobrazeno 1 - 10
of 448
pro vyhledávání: '"Durante, Fabrizio"'
Autor:
Gallo, Daniela, Liguori, Angelica, Ritacco, Ettore, Caviglione, Luca, Durante, Fabrizio, Manco, Giuseppe
To achieve accurate and unbiased predictions, Machine Learning (ML) models rely on large, heterogeneous, and high-quality datasets. However, this could raise ethical and legal concerns regarding copyright and authorization aspects, especially when in
Externí odkaz:
http://arxiv.org/abs/2410.05819
Recently, the original storage prescription for the Hopfield model of neural networks -- as well as for its dense generalizations -- has been turned into a genuine Hebbian learning rule by postulating the expression of its Hamiltonian for both the su
Externí odkaz:
http://arxiv.org/abs/2401.07110
Autor:
Aquaro, Miriam, Alemanno, Francesco, Kanter, Ido, Durante, Fabrizio, Agliari, Elena, Barra, Adriano
The gap between the huge volumes of data needed to train artificial neural networks and the relatively small amount of data needed by their biological counterparts is a central puzzle in machine learning. Here, inspired by biological information-proc
Externí odkaz:
http://arxiv.org/abs/2204.07954
This paper examines the dependence between electricity prices, demand, and renewable energy sources by means of a multivariate copula model {while studying Germany, the widest studied market in Europe}. The inter-dependencies are investigated in-dept
Externí odkaz:
http://arxiv.org/abs/2201.01132
Autor:
Agliari, Elena, Alemanno, Francesco, Aquaro, Miriam, Barra, Adriano, Durante, Fabrizio, Kanter, Ido
Publikováno v:
In Neural Networks May 2024 173
The univariate distorted distribution were introduced in risk theory to represent changes (distortions) in the expected distributions of some risks. Later they were also applied to represent distributions of order statistics, coherent systems, propor
Externí odkaz:
http://arxiv.org/abs/2010.14224
A theoretical framework is presented for a (copula-based) notion of dissimilarity between continuous random vectors and its main properties are studied. The proposed dissimilarity assigns the smallest value to a pair of random vectors that are comono
Externí odkaz:
http://arxiv.org/abs/2007.04799
Autor:
Benevento, Alessia1 (AUTHOR) alessia.benevento@unisalento.it, Durante, Fabrizio1 (AUTHOR) fabrizio.durante@unisalento.it
Publikováno v:
Mathematics (2227-7390). Jan2024, Vol. 12 Issue 1, p67. 15p.
Akademický článek
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