Zobrazeno 1 - 10
of 337
pro vyhledávání: '"Stramaglia, Sebastiano"'
Autor:
Mijatovic, Gorana, Antonacci, Yuri, Javorka, Michal, Marinazzo, Daniele, Stramaglia, Sebastiano, Faes, Luca
Many complex systems in science and engineering are modeled as networks whose nodes and links depict the temporal evolution of each system unit and the dynamic interaction between pairs of units, which are assessed respectively using measures of auto
Externí odkaz:
http://arxiv.org/abs/2408.15617
Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively use it for dynamic system evolution, is a key component of neural information processing. Information storage (IS), the main measure
Externí odkaz:
http://arxiv.org/abs/2408.15875
This paper studies emergent phenomena in neural networks by focusing on grokking where models suddenly generalize after delayed memorization. To understand this phase transition, we utilize higher-order mutual information to analyze the collective be
Externí odkaz:
http://arxiv.org/abs/2408.08944
Transfer Entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias - either in deficiency or excess - during both pairwise and conditioned calculations, owing to high-order dependencies among
Externí odkaz:
http://arxiv.org/abs/2402.03229
Autor:
Mijatovic, Gorana, Sparacino, Laura, Antonacci, Yuri, Javorka, Michal, Marinazzo, Daniele, Stramaglia, Sebastiano, Faes, Luca
The network representation is becoming increasingly popular for the description of cardiovascular interactions based on the analysis of multiple simultaneously collected variables. However, the traditional methods to assess network links based on pai
Externí odkaz:
http://arxiv.org/abs/2401.05556
Autor:
Javarone, Marco Alberto, Rosas, Fernando E., Facchi, Paolo, Pascazio, Saverio, Stramaglia, Sebastiano
Publikováno v:
Phys. Rev. A 109, 042605 (2024)
Here, we leverage recent advances in information theory to develop a novel method to characterise the dominant character of the high-order dependencies of quantum systems. To this end, we introduce the Q-information: an information-theoretic measure
Externí odkaz:
http://arxiv.org/abs/2310.03681
Quantifying which neurons are important with respect to the classification decision of a trained neural network is essential for understanding their inner workings. Previous work primarily attributed importance to individual neurons. In this work, we
Externí odkaz:
http://arxiv.org/abs/2211.00416
Autor:
Scagliarini, Tomas, Pappalardo, Giuseppe, Biondo, Alessio Emanuele, Pluchino, Alessandro, Rapisarda, Andrea, Stramaglia, Sebastiano
In this paper we analyse the effects of information flows in cryptocurrency markets. We first define a cryptocurrency trading network, i.e. the network made using cryptocurrencies as nodes and the Granger causality among their weekly log returns as l
Externí odkaz:
http://arxiv.org/abs/2207.04004
Autor:
Scagliarini, Tomas, Nuzzi, Davide, Antonacci, Yuri, Faes, Luca, Rosas, Fernando E., Marinazzo, Daniele, Stramaglia, Sebastiano
O-information is an information-theoretic metric that captures the overall balance between redundant and synergistic information shared by groups of three or more variables. To complement the global assessment provided by this metric, here we propose
Externí odkaz:
http://arxiv.org/abs/2207.03581
Autor:
Marinazzo, Daniele, Van Roozendaal, Jan, Rosas, Fernando E., Stella, Massimo, Comolatti, Renzo, Colenbier, Nigel, Stramaglia, Sebastiano, Rosseel, Yves
Publikováno v:
Behavior Research Methods, 2024
Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated cross-sectional, time-series, or panel data. These networks consti
Externí odkaz:
http://arxiv.org/abs/2205.01035