Unsupervised learning of invariant representations

[1]  Joel Z. Leibo,et al.  The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex , 2014, bioRxiv.

[2]  Tomaso Poggio,et al.  Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? , 2013, 1311.4158.

[3]  Lorenzo Rosasco,et al.  Word-level invariant representations from acoustic waveforms , 2014, INTERSPEECH.

[4]  Lorenzo Rosasco,et al.  Phone classification by a hierarchy of invariant representation layers , 2014, INTERSPEECH.

[5]  Joel Z. Leibo,et al.  Learning invariant representations and applications to face verification , 2013, NIPS.

[6]  Lorenzo Rosasco,et al.  The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). , 2012 .

[7]  Joel Z. Leibo,et al.  Why The Brain Separates Face Recognition From Object Recognition , 2011, NIPS.

[8]  Stefano Soatto,et al.  Steps Towards a Theory of Visual Information: Active Perception, Signal-to-Symbol Conversion and the Interplay Between Sensing and Control , 2011, ArXiv.

[9]  Stefano Soatto,et al.  Video-based descriptors for object recognition , 2011, Image Vis. Comput..

[10]  Stéphane Mallat,et al.  Group Invariant Scattering , 2011, ArXiv.

[11]  T. Poggio,et al.  What and where: A Bayesian inference theory of attention , 2010, Vision Research.

[12]  N. Kanwisher Functional specificity in the human brain: A window into the functional architecture of the mind , 2010, Proceedings of the National Academy of Sciences.

[13]  Lorenzo Rosasco,et al.  Publisher Accessed Terms of Use Detailed Terms Mathematics of the Neural Response , 2022 .

[14]  David D. Cox,et al.  A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation , 2009, PLoS Comput. Biol..

[15]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[16]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[17]  E. Candès,et al.  Sparsity and incoherence in compressive sampling , 2006, math/0611957.

[18]  Stuart Geman,et al.  Invariance and selectivity in the ventral visual pathway , 2006, Journal of Physiology-Paris.

[19]  David G. Lowe,et al.  Multiclass Object Recognition with Sparse, Localized Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[20]  D. George,et al.  A hierarchical Bayesian model of invariant pattern recognition in the visual cortex , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[21]  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 .

[22]  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..

[23]  A. Koloydenko Symmetric measures via moments , 2004, math/0406173.

[24]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[25]  Doris Y. Tsao,et al.  Faces and objects in macaque cerebral cortex , 2003, Nature Neuroscience.

[26]  P. Lennie Receptive fields , 2003, Current Biology.

[27]  Tomaso Poggio,et al.  Models of object recognition , 2000, Nature Neuroscience.

[28]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[29]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[30]  Hanns Schulz-Mirbach Constructing invariant features by averaging techniques , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[31]  Peter Földiák,et al.  Learning Invariance from Transformation Sequences , 1991, Neural Comput..

[32]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[33]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[34]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[35]  Tomaso Poggio,et al.  From Understanding Computation to Understanding Neural Circuitry , 1976 .

[36]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[37]  H. Chick,et al.  The photosensitizing action of buckwheat (Fagopyrum esculentum) , 1941, The Journal of physiology.

[38]  H. Wold,et al.  Some Theorems on Distribution Functions , 1936 .