Zobrazeno 1 - 10
of 269
pro vyhledávání: '"Mediano, Pedro"'
Autor:
Liardi, Alberto, Rosas, Fernando E., Carhart-Harris, Robin L., Blackburne, George, Bor, Daniel, Mediano, Pedro A. M.
A key feature of information theory is its universality, as it can be applied to study a broad variety of complex systems. However, many information-theoretic measures can vary significantly even across systems with similar properties, making normali
Externí odkaz:
http://arxiv.org/abs/2410.11583
High-order phenomena play crucial roles in many systems of interest, but their analysis is often highly nontrivial. There is a rich literature providing a number of alternative information-theoretic quantities capturing high-order phenomena, but thei
Externí odkaz:
http://arxiv.org/abs/2410.10485
The partial information decomposition (PID) and its extension integrated information decomposition ($\Phi$ID) are promising frameworks to investigate information phenomena involving multiple variables. An important limitation of these approaches is t
Externí odkaz:
http://arxiv.org/abs/2410.06224
Autor:
Dominé, Clémentine C. J., Anguita, Nicolas, Proca, Alexandra M., Braun, Lukas, Kunin, Daniel, Mediano, Pedro A. M., Saxe, Andrew M.
Biological and artificial neural networks develop internal representations that enable them to perform complex tasks. In artificial networks, the effectiveness of these models relies on their ability to build task specific representation, a process i
Externí odkaz:
http://arxiv.org/abs/2409.14623
Many information-theoretic quantities have corresponding representations in terms of sets. The prevailing signed measure space for characterising entropy, the $I$-measure of Yeung, is occasionally unable to discern between qualitatively distinct syst
Externí odkaz:
http://arxiv.org/abs/2409.04845
The Shannon entropy of a random variable X has much behaviour analogous to a signed measure. Previous work has explored this connection by defining a signed measure on abstract sets, which are taken to represent the information that different random
Externí odkaz:
http://arxiv.org/abs/2409.03732
Biological neural networks can perform complex computations to predict their environment, far above the limited predictive capabilities of individual neurons. While conventional approaches to understanding these computations often focus on isolating
Externí odkaz:
http://arxiv.org/abs/2406.19201
Causal discovery in time series is a rapidly evolving field with a wide variety of applications in other areas such as climate science and neuroscience. Traditional approaches assume a stationary causal graph, which can be adapted to nonstationary ti
Externí odkaz:
http://arxiv.org/abs/2406.17698
Systems of interest for theoretical or experimental work often exhibit high-order interactions, corresponding to statistical interdependencies in groups of variables that cannot be reduced to dependencies in subsets of them. While still under active
Externí odkaz:
http://arxiv.org/abs/2404.07140
Autor:
Rosas, Fernando E., Geiger, Bernhard C., Luppi, Andrea I, Seth, Anil K., Polani, Daniel, Gastpar, Michael, Mediano, Pedro A. M.
Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent advances have introduced tools to characterise emergent macroscopic level
Externí odkaz:
http://arxiv.org/abs/2402.09090