Turner R, Sahani M. (2007). A maximum-likelihood interpretation for slow feature analysis. Neural computation. 19 [PubMed]

See more from authors: Turner R · Sahani M

References and models cited by this paper

Attias H. (1999). Independent factor analysis. Neural computation. 11 [PubMed]

Bell AJ, Sejnowski TJ. (1997). The "independent components" of natural scenes are edge filters. Vision research. 37 [PubMed]

Berkes P, Wiskott L. (2005). Slow feature analysis yields a rich repertoire of complex cell properties. Journal of vision. 5 [PubMed]

Bishop C, Tipping M. (1999). Probabilistic principal component analysis J Roy Stat Soc B. 61

Blaschke T, Berkes P, Wiskott L. (2006). What is the relation between slow feature analysis and independent component analysis? Neural computation. 18 [PubMed]

Dempster AP, Laird NM, Rubin DB. (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 39

Foldiak P. (1991). Learning invariance from transformation sequences Neural Comput. 3

Ghahramani Z, Jordan MI, Jaakkola TS, Saul LK. (1999). An introduction to variational methods for graphical models Mach Learn. 37

Ghahramani Z, Roweis ST. (1999). Learning nonlinear dynamical systems using an EM algorithm Advances in neural information processing systems. 11

Grimes DB, Rao RP. (2005). Bilinear sparse coding for invariant vision. Neural computation. 17 [PubMed]

Hinton G. (1989). Connectionist learning procedures Art Intell. 40

Hinton GE. (2002). Training products of experts by minimizing contrastive divergence. Neural computation. 14 [PubMed]

Hinton GE, Neal RM. (1998). A new view of the EM algorithm that justifies incremental, sparse and other variants Learning in graphical models.

Hurri J, Hyvärinen A. (2003). Temporal and spatiotemporal coherence in simple-cell responses: a generative model of natural image sequences. Network (Bristol, England). 14 [PubMed]

Hyvärinen A, Hurri J, Väyrynen J. (2003). Bubbles: a unifying framework for low-level statistical properties of natural image sequences. Journal of the Optical Society of America. A, Optics, image science, and vision. 20 [PubMed]

Karklin Y, Lewicki MS. (2005). A hierarchical Bayesian model for learning nonlinear statistical regularities in nonstationary natural signals. Neural computation. 17 [PubMed]

Kayser C, Körding KP, König P. (2003). Learning the nonlinearity of neurons from natural visual stimuli. Neural computation. 15 [PubMed]

Körding KP, Kayser C, Einhäuser W, König P. (2004). How are complex cell properties adapted to the statistics of natural stimuli? Journal of neurophysiology. 91 [PubMed]

Mackay DJC. (1999). Maximum likelihood and covariant algorithms for independent component analysis Unpublished manuscript. Available online at http:--wol.ra.phy.cam.ac.uk-pub-mackay-ica.ps.gz.

Miskin J. (2000). Ensemble learning for independent components analysis Unpublished doctoral dissertation, Cambridge University.

Mitchison G. (1991). Removing time variation with the anti-Hebbian differential synapse Neural Comput. 3

Olshausen BA, Field DJ. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature. 381 [PubMed]

Osindero S, Welling M, Hinton GE. (2005). Topographic Product Models Applied to Natural Scene Statistics Neural Comput. 18

Parra LC, Pearlmutter BA. (1997). Maximum likelihood blind source separation:A context-sensitive generalization of ICA Advances in neural information processing systems. 9

Roweis S, Ghahramani Z. (1999). A unifying review of linear gaussian models. Neural computation. 11 [PubMed]

Stone JV. (1996). Learning perceptually salient visual parameters using spatiotemporal smoothness constraints. Neural computation. 8 [PubMed]

Tenenbaum JB, Freeman WT. (2000). Separating style and content with bilinear models. Neural computation. 12 [PubMed]

Valpola H, Sarela J. (2005). Denoising source separation J Machine Learning Res. 6

Wiskott L, Sejnowski TJ. (2002). Slow feature analysis: unsupervised learning of invariances. Neural computation. 14 [PubMed]

References and models that cite this paper
This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.