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[1] K. Obermayer,et al. Geometry of orientation and ocular dominance columns in monkey striate cortex , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[2] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[3] M. Stryker,et al. Ocular dominance peaks at pinwheel center singularities of the orientation map in cat visual cortex. , 1997, Journal of neurophysiology.
[4] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[5] Aapo Hyvärinen,et al. A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images , 2001, Vision Research.
[6] Geoffrey E. Hinton,et al. Products of Hidden Markov Models , 2001, AISTATS.
[7] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[8] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[9] Laurenz Wiskott,et al. Slow feature analysis yields a rich repertoire of complex cell properties. , 2005, Journal of vision.
[10] Cordelia Schmid,et al. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[11] Thomas Serre,et al. A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.
[12] Bruno A. Olshausen,et al. Learning Transformational Invariants from Natural Movies , 2008, NIPS.
[13] Nicolas Pinto,et al. Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..
[14] Yoshua Bengio,et al. Slow, Decorrelated Features for Pretraining Complex Cell-like Networks , 2009, NIPS.
[15] Yihong Gong,et al. Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[16] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[17] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[18] Geoffrey E. Hinton,et al. Factored conditional restricted Boltzmann Machines for modeling motion style , 2009, ICML '09.
[19] R. Fergus,et al. Learning invariant features through topographic filter maps , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[20] Richard E. Turner,et al. A Structured Model of Video Reproduces Primary Visual Cortical Organisation , 2009, PLoS Comput. Biol..
[21] Yihong Gong,et al. Nonlinear Learning using Local Coordinate Coding , 2009, NIPS.
[22] David D. Cox,et al. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation , 2009, PLoS Comput. Biol..
[23] Michael S. Lewicki,et al. Emergence of complex cell properties by learning to generalize in natural scenes , 2009, Nature.
[24] Jean Ponce,et al. Learning mid-level features for recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[25] Marc'Aurelio Ranzato,et al. Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition , 2010, ArXiv.
[26] Niko Wilbert,et al. Slow feature analysis , 2011, Scholarpedia.