A Maximum-Likelihood Interpretation for Slow Feature Analysis
暂无分享,去创建一个
[1] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[2] Laurenz Wiskott,et al. What Is the Relation Between Slow Feature Analysis and Independent Component Analysis? , 2006, Neural Computation.
[3] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[4] Michael S. Lewicki,et al. A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals , 2005, Neural Computation.
[5] A. Hyvärinen,et al. Temporal and spatiotemporal coherence in simple-cell responses: a generative model of natural image sequences , 2003, Network.
[6] Harri Valpola,et al. Denoising Source Separation , 2005, J. Mach. Learn. Res..
[7] James V. Stone. Learning Perceptually Salient Visual Parameters Using Spatiotemporal Smoothness Constraints , 1996, Neural Computation.
[8] Konrad Paul Kording,et al. How are complex cell properties adapted to the statistics of natural stimuli? , 2004, Journal of neurophysiology.
[9] Graeme Mitchison,et al. Removing Time Variation with the Anti-Hebbian Differential Synapse , 1991, Neural Computation.
[10] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[11] Barak A. Pearlmutter,et al. Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA , 1996, NIPS.
[12] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[13] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[14] Rajesh P. N. Rao,et al. Bilinear Sparse Coding for Invariant Vision , 2005, Neural Computation.
[15] Joshua B. Tenenbaum,et al. Separating Style and Content with Bilinear Models , 2000, Neural Computation.
[16] Laurenz Wiskott,et al. Slow feature analysis yields a rich repertoire of complex cell properties. , 2005, Journal of vision.
[17] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[18] Konrad P. Körding,et al. Learning the Nonlinearity of Neurons from Natural Visual Stimuli , 2003, Neural Computation.
[19] Harri Lappalainen,et al. Ensemble learning for independent component analysis , 1999 .
[20] Hagai Attias,et al. Independent Factor Analysis , 1999, Neural Computation.
[21] Aapo Hyvärinen,et al. Bubbles: a unifying framework for low-level statistical properties of natural image sequences. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.
[22] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[23] Geoffrey E. Hinton. Connectionist Learning Procedures , 1989, Artif. Intell..
[24] Zoubin Ghahramani,et al. A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.
[25] A. Hyvärinen,et al. Temporal and spatiotemporal coherence in simple-cell responses: a generative model of natural image sequences , 2003 .
[26] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine-mediated learning.
[27] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[28] Zoubin Ghahramani,et al. Learning Nonlinear Dynamical Systems Using an EM Algorithm , 1998, NIPS.
[29] Yen-Wei Chen,et al. Ensemble learning for independent component analysis , 2006, Pattern Recognit..
[30] Geoffrey E. Hinton,et al. Topographic Product Models Applied to Natural Scene Statistics , 2006, Neural Computation.