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ICANN
2005
Springer
15 years 4 months ago
Learning Features of Intermediate Complexity for the Recognition of Biological Motion
Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have n...
Rodrigo Sigala, Thomas Serre, Tomaso Poggio, Marti...
CVPR
2009
IEEE
16 years 6 months ago
Learning Invariant Features Through Topographic Filter Maps
Several recently-proposed architectures for highperformance object recognition are composed of two main stages: a feature extraction stage that extracts locallyinvariant feature...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergu...
NIPS
2008
15 years 15 days ago
Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depe...
Francis Bach
109
Voted
ICCV
2011
IEEE
13 years 11 months ago
A Linear Subspace Learning Approach via Sparse Coding
Linear subspace learning (LSL) is a popular approach to image recognition and it aims to reveal the essential features of high dimensional data, e.g., facial images, in a lower di...
Lei Zhang, Pengfei Zhu, Qinghu Hu, David Zhang
60
Voted
JOCN
2011
65views more  JOCN 2011»
14 years 6 months ago
Right Hemisphere Dominance in Visual Statistical Learning
■ Several studies report a right hemisphere advantage for visuospatial integration and a left hemisphere advantage for inferring conceptual knowledge from patterns of covariatio...
Matthew E. Roser, József Fiser, Richard N. ...