Unsupervised learning of invariant representations
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Lorenzo Rosasco | Joel Z. Leibo | Tomaso A. Poggio | Andrea Tacchetti | Jim Mutch | Fabio Anselmi | T. Poggio | A. Tacchetti | L. Rosasco | Jim Mutch | F. Anselmi
[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 .