Unsupervised feature learning improves prediction of human brain activity in response to natural images.
about
Gaussian mixture models and semantic gating improve reconstructions from human brain activity.The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.Classifying four-category visual objects using multiple ERP components in single-trial ERPModeling the Dynamics of Human Brain Activity with Recurrent Neural Networks.Encoding and Decoding Models in Cognitive Electrophysiology.Representations of naturalistic stimulus complexity in early and associative visual and auditory cortices.
P2860
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P2860
Unsupervised feature learning improves prediction of human brain activity in response to natural images.
description
2014 nî lūn-bûn
@nan
2014 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Unsupervised feature learning ...... in response to natural images.
@ast
Unsupervised feature learning ...... in response to natural images.
@en
type
label
Unsupervised feature learning ...... in response to natural images.
@ast
Unsupervised feature learning ...... in response to natural images.
@en
prefLabel
Unsupervised feature learning ...... in response to natural images.
@ast
Unsupervised feature learning ...... in response to natural images.
@en
P2860
P1476
Unsupervised feature learning ...... in response to natural images.
@en
P2093
Marcel A J van Gerven
P2860
P304
P356
10.1371/JOURNAL.PCBI.1003724
P50
P577
2014-08-07T00:00:00Z