Cargando…
Multiplex visibility graphs to investigate recurrent neural network dynamics
A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and...
Autores principales: | Bianchi, Filippo Maria, Livi, Lorenzo, Alippi, Cesare, Jenssen, Robert |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345088/ https://www.ncbi.nlm.nih.gov/pubmed/28281563 http://dx.doi.org/10.1038/srep44037 |
Ejemplares similares
-
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere
por: Verzelli, Pietro, et al.
Publicado: (2019) -
Recurrent neural networks for short-term load forecasting: an overview and comparative analysis
por: Bianchi, Filippo Maria, et al.
Publicado: (2017) -
Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks
por: Sannino, Speranza, et al.
Publicado: (2017) -
Graph rules for recurrent neural network dynamics: extended version
por: Curto, Carina, et al.
Publicado: (2023) -
Artificial intelligence in the age of neural networks and brain computing
por: Kozma, Robert, et al.
Publicado: (2018)