The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).
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Lorenzo Rosasco | Joel Z. Leibo | Tomaso Poggio | Andrea Tacchetti | Jim Mutch | T. Poggio | A. Tacchetti | L. Rosasco | Jim Mutch
[1] Doris Y. Tsao,et al. Patches with Links: A Unified System for Processing Faces in the Macaque Temporal Lobe , 2008, Science.
[2] Geoffrey E. Hinton,et al. Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines , 2010, Neural Computation.
[3] Joel Z. Leibo,et al. Learning Generic Invariances in Object Recognition: Translation and Scale , 2010 .
[4] W. M. Keck,et al. Highly Selective Receptive Fields in Mouse Visual Cortex , 2008, The Journal of Neuroscience.
[5] Doris Y. Tsao,et al. Faces and objects in macaque cerebral cortex , 2003, Nature Neuroscience.
[6] Eero P. Simoncelli,et al. Metamers of the ventral stream , 2011, Nature Neuroscience.
[7] Tomaso Poggio,et al. Models of object recognition , 2000, Nature Neuroscience.
[8] Joel Z. Leibo,et al. How can cells in the anterior medial face patch be viewpoint invariant , 2011 .
[9] J. Hegdé,et al. Selectivity for Complex Shapes in Primate Visual Area V2 , 2000, The Journal of Neuroscience.
[10] Syed Twareque Ali,et al. Two-Dimensional Wavelets and their Relatives , 2004 .
[11] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[12] Nancy Kanwisher,et al. A cortical representation of the local visual environment , 1998, Nature.
[13] J. P. Jones,et al. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.
[14] Antonio Torralba,et al. Statistics of natural image categories , 2003, Network.
[15] L. Maffei,et al. Spontaneous impulse activity of rat retinal ganglion cells in prenatal life. , 1988, Science.
[16] Charles F Stevens. Preserving properties of object shape by computations in primary visual cortex. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[17] D. Ringach. Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. , 2002, Journal of neurophysiology.
[18] S. Nelson,et al. Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.
[19] L. Rosasco. THE COMPUTATIONAL MAGIC OF THE VENTRAL STREAM , 2011 .
[20] Doris Y. Tsao,et al. A Cortical Region Consisting Entirely of Face-Selective Cells , 2006, Science.
[21] Tomaso A. Poggio,et al. A Canonical Neural Circuit for Cortical Nonlinear Operations , 2008, Neural Computation.
[22] Niko Wilbert,et al. Invariant Object Recognition and Pose Estimation with Slow Feature Analysis , 2011, Neural Computation.
[23] J. Austin. Associative memory , 1987 .
[24] S. Gerber,et al. Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex , 2008 .
[25] Rajesh P. N. Rao,et al. Learning Lie Groups for Invariant Visual Perception , 1998, NIPS.
[26] M. Ferraro,et al. Relationship between integral transform invariances and Lie group theory , 1988 .
[27] N. Kanwisher,et al. Visual word processing and experiential origins of functional selectivity in human extrastriate cortex , 2007, Proceedings of the National Academy of Sciences.
[28] Cosimo Urgesi,et al. Magnetic Stimulation of Extrastriate Body Area Impairs Visual Processing of Nonfacial Body Parts , 2004, Current Biology.
[29] W. Hoffman. The Lie algebra of visual perception , 1966 .
[30] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[31] D. V. van Essen,et al. Selectivity for polar, hyperbolic, and Cartesian gratings in macaque visual cortex. , 1993, Science.
[32] Joel Z. Leibo,et al. Why The Brain Separates Face Recognition From Object Recognition , 2011, NIPS.
[33] W. Pitts,et al. How we know universals; the perception of auditory and visual forms. , 1947, The Bulletin of mathematical biophysics.
[34] Y. LeCun,et al. Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[35] Edmund T. Rolls,et al. Invariant Object Recognition in the Visual System with Novel Views of 3D Objects , 2002, Neural Computation.
[36] David Cox,et al. Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook , 2011, CVPR 2011 WORKSHOPS.
[37] R. Pérez,et al. Perception of Random Dot Interference Patterns , 1973, Nature.
[38] P. Downing,et al. Selectivity for the human body in the fusiform gyrus. , 2005, Journal of neurophysiology.
[39] Jean-Michel Morel,et al. ASIFT: An Algorithm for Fully Affine Invariant Comparison , 2011, Image Process. Line.
[40] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.
[41] C. Shatz,et al. Transient period of correlated bursting activity during development of the mammalian retina , 1993, Neuron.
[42] J. Koenderink. The brain a geometry engine , 1990, Psychological research.
[43] Doris Y. Tsao,et al. Functional Compartmentalization and Viewpoint Generalization Within the Macaque Face-Processing System , 2010, Science.
[44] Erkki Oja,et al. Principal components, minor components, and linear neural networks , 1992, Neural Networks.
[45] N. Kanwisher,et al. A Cortical Area Selective for Visual Processing of the Human Body , 2001, Science.
[46] Joel Z. Leibo,et al. Learning and disrupting invariance in visual recognition with a temporal association rule , 2011, Front. Comput. Neurosci..
[47] J. DiCarlo,et al. Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex , 2010, Neuron.
[48] T. Poggio,et al. On optimal nonlinear associative recall , 1975, Biological Cybernetics.
[49] R C Reid,et al. Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory , 1996, The Journal of Neuroscience.
[50] Yann LeCun,et al. Learning Invariant Feature Hierarchies , 2012, ECCV Workshops.
[51] Zhenghao Chen,et al. On Random Weights and Unsupervised Feature Learning , 2011, ICML.
[52] D. Ruderman. The statistics of natural images , 1994 .
[53] J. Devlin,et al. Triple Dissociation of Faces, Bodies, and Objects in Extrastriate Cortex , 2009, Current Biology.
[54] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[55] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[56] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[57] S Lehéricy,et al. The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. , 2000, Brain : a journal of neurology.
[58] Stéphane Mallat,et al. Group Invariant Scattering , 2011, ArXiv.
[59] Marc'Aurelio Ranzato,et al. Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[60] A. Leff,et al. Structural anatomy of pure and hemianopic alexia , 2006, Journal of Neurology, Neurosurgery & Psychiatry.
[61] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[62] Thomas Serre,et al. Learning complex cell invariance from natural videos: A plausibility proof , 2007 .
[63] T. Poggio,et al. Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries , 1992 .
[64] Y. Meyer,et al. Wavelets and Filter Banks , 1991 .
[65] Thomas Serre,et al. A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , 2005 .
[66] Scott D. Slotnick,et al. The Visual Word Form Area , 2013 .
[67] K. Gröchenig. Multivariate Gabor frames and sampling of entire functions of several variables , 2011 .
[68] Juha Karhunen,et al. Stability of Oja's PCA Subspace Rule , 1994, Neural Computation.
[69] H. Bülthoff,et al. Effects of temporal association on recognition memory , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[70] Ronen Basri,et al. Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[71] Yoshua Bengio,et al. Scaling learning algorithms towards AI , 2007 .
[72] Thomas Serre,et al. A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.
[73] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[74] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[75] J. DiCarlo,et al. 'Breaking' position-invariant object recognition , 2005, Nature Neuroscience.
[76] Tomaso Poggio,et al. Learning to discount transformations as the computational goal of visual cortex , 2011 .
[77] C. Urgesi,et al. The Neural Basis of Body Form and Body Action Agnosia , 2008, Neuron.
[78] Terence D. Sanger,et al. Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.
[79] Tomaso Poggio,et al. From primal templates to invariant recognition , 2010 .
[80] A. Grossmann,et al. TRANSFORMS ASSOCIATED TO SQUARE INTEGRABLE GROUP REPRESENTATION. 2. EXAMPLES , 1986 .