A hierarchical Bayesian model for learning nonlinear statistical regularities in nonstationary natural signals.
about
Incorporating naturalistic correlation structure improves spectrogram reconstruction from neuronal activity in the songbird auditory midbrain.A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays.Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.Nonlinear extraction of independent components of natural images using radial gaussianization.Natural image coding in V1: how much use is orientation selectivity?A structured model of video reproduces primary visual cortical organisation.Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics.Sparsity-regularized HMAX for visual recognition.A maximum-likelihood interpretation for slow feature analysis.Simultaneous Estimation of Nongaussian Components and Their Correlation Structure.Perceptual organization in the tilt illusionPredictive coding and the slowness principle: an information-theoretic approach.Unifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis.Cascaded Amplitude Modulations in Sound Texture Perception.A biologically plausible learning rule for the Infomax on recurrent neural networks.Computing local edge probability in natural scenes from a population of oriented simple cells.Visual attention and flexible normalization pools.Soft mixer assignment in a hierarchical generative model of natural scene statistics.Independent component analysis: recent advances.An early underwater artificial vision model in ocean investigations via independent component analysis.The impact on midlevel vision of statistically optimal divisive normalization in V1.Image modeling and denoising with orientation-adapted Gaussian scale mixtures.Information maximization principle explains the emergence of complex cell-like neurons.Modeling multiscale subbands of photographic images with fields of Gaussian scale mixtures.Learning intermediate-level representations of form and motion from natural movies.A two-layer model of natural stimuli estimated with score matching.Replicating receptive fields of simple and complex cells in primary visual cortex in a neuronal network model with temporal and population sparseness and reliability.Learning Midlevel Auditory Codes from Natural Sound Statistics.Emergence of complex cell properties by learning to generalize in natural scenes.Improved sparse coding under the influence of perceptual attention.Learning nonlinear statistical regularities in natural images by modeling the outer product of image intensities.Topographic product models applied to natural scene statistics.Correlated topographic analysis: estimating an ordering of correlated componentsLearning Natural Image Structure with a Horizontal Product Model
P2860
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P2860
A hierarchical Bayesian model for learning nonlinear statistical regularities in nonstationary natural signals.
description
2005 nî lūn-bûn
@nan
2005年の論文
@ja
2005年学术文章
@wuu
2005年学术文章
@zh-cn
2005年学术文章
@zh-hans
2005年学术文章
@zh-my
2005年学术文章
@zh-sg
2005年學術文章
@yue
2005年學術文章
@zh
2005年學術文章
@zh-hant
name
A hierarchical Bayesian model ...... nonstationary natural signals.
@en
A hierarchical Bayesian model ...... nonstationary natural signals.
@nl
type
label
A hierarchical Bayesian model ...... nonstationary natural signals.
@en
A hierarchical Bayesian model ...... nonstationary natural signals.
@nl
prefLabel
A hierarchical Bayesian model ...... nonstationary natural signals.
@en
A hierarchical Bayesian model ...... nonstationary natural signals.
@nl
P2860
P356
P1433
P1476
A hierarchical Bayesian model ...... nonstationary natural signals.
@en
P2093
Michael S Lewicki
Yan Karklin
P2860
P304
P356
10.1162/0899766053011474
P577
2005-02-01T00:00:00Z