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» Receptive Field Structures for Recognition
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NIPS
2004
13 years 9 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra
NECO
2006
111views more  NECO 2006»
13 years 7 months ago
Dynamics and Topographic Organization of Recursive Self-Organizing Maps
Recently, there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, at present, ...
Peter Tiño, Igor Farkas, Jort van Mourik
TCSV
2008
313views more  TCSV 2008»
13 years 7 months ago
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
Efficiently and accurately detecting pedestrians plays a very important role in many computer vision applications such as video surveillance and smart cars. In order to find the ri...
Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhan...
ICDAR
2007
IEEE
14 years 1 months ago
Energy-Based Models in Document Recognition and Computer Vision
The Machine Learning and Pattern Recognition communities are facing two challenges: solving the normalization problem, and solving the deep learning problem. The normalization pro...
Yann LeCun, Sumit Chopra, Marc'Aurelio Ranzato, Fu...
ECCV
2006
Springer
14 years 9 months ago
TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation
Abstract. This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned ...
Jamie Shotton, John M. Winn, Carsten Rother, Anton...