Cargando…
Limit Theorems as Blessing of Dimensionality: Neural-Oriented Overview
As a system becomes more complex, at first, its description and analysis becomes more complicated. However, a further increase in the system’s complexity often makes this analysis simpler. A classical example is Central Limit Theorem: when we have a few independent sources of uncertainty, the result...
Autores principales: | Kreinovich, Vladik, Kosheleva, Olga |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145334/ https://www.ncbi.nlm.nih.gov/pubmed/33922277 http://dx.doi.org/10.3390/e23050501 |
Ejemplares similares
-
Why Spiking Neural Networks Are Efficient: A Theorem
por: Beer, Michael, et al.
Publicado: (2020) -
Which Distributions (or Families of Distributions) Best Represent Interval Uncertainty: Case of Permutation-Invariant Criteria
por: Beer, Michael, et al.
Publicado: (2020) -
Why Dilated Convolutional Neural Networks: A Proof of Their Optimality
por: Contreras, Jonatan, et al.
Publicado: (2021) -
Uncertainty modeling: dedicated to professor Boris Kovalerchuk on his anniversary
por: Kreinovich, Vladik
Publicado: (2017) -
Uncertainty modeling: dedicated to professor Boris Kovalerchuk on his anniversary
por: Kreinovich, Vladik
Publicado: (2017)