Deep, narrow sigmoid belief networks are universal approximators.
about
Learning to represent visual input.A deep learning framework for financial time series using stacked autoencoders and long-short term memoryRefinements of universal approximation results for deep belief networks and restricted Boltzmann machines.Universal approximation depth and errors of narrow belief networks with discrete units.Deep Convolutional Neural Networks for Hyperspectral Image Classification
P2860
Deep, narrow sigmoid belief networks are universal approximators.
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年学术文章
@wuu
2008年学术文章
@zh-cn
2008年学术文章
@zh-hans
2008年学术文章
@zh-my
2008年学术文章
@zh-sg
2008年學術文章
@yue
2008年學術文章
@zh
2008年學術文章
@zh-hant
name
Deep, narrow sigmoid belief networks are universal approximators.
@en
Deep, narrow sigmoid belief networks are universal approximators.
@nl
type
label
Deep, narrow sigmoid belief networks are universal approximators.
@en
Deep, narrow sigmoid belief networks are universal approximators.
@nl
prefLabel
Deep, narrow sigmoid belief networks are universal approximators.
@en
Deep, narrow sigmoid belief networks are universal approximators.
@nl
P2860
P1433
P1476
Deep, narrow sigmoid belief networks are universal approximators.
@en
P2860
P304
P356
10.1162/NECO.2008.12-07-661
P577
2008-11-01T00:00:00Z