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ICML
1995
IEEE
14 years 11 months ago
Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network
Understanding high-dimensional real world data usually requires learning the structure of the data space. The structure maycontain high-dimensional clusters that are related in co...
Justine Blackmore, Risto Miikkulainen
ICRA
2005
IEEE
129views Robotics» more  ICRA 2005»
14 years 3 months ago
Fast Computational Methods for Visually Guided Robots
— This paper proposes numerical algorithms for reducing the computational cost of semi-supervised and active learning procedures for visually guided mobile robots from O(M3 ) to ...
Maryam Mahdaviani, Nando de Freitas, Bob Fraser, F...
GECCO
2009
Springer
204views Optimization» more  GECCO 2009»
14 years 2 months ago
Combined structure and motion extraction from visual data using evolutionary active learning
We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are c...
Krishnanand N. Kaipa, Josh C. Bongard, Andrew N. M...
CVPR
2008
IEEE
15 years 1 days ago
Visual tracking via incremental Log-Euclidean Riemannian subspace learning
Recently, a novel Log-Euclidean Riemannian metric [28] is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means ...
Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, ...
CVPR
2006
IEEE
14 years 4 months ago
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
We present an algorithm for jointly learning a consistent bidirectional generative-recognition model that combines top-down and bottom-up processing for monocular 3d human motion ...
Cristian Sminchisescu, Atul Kanaujia, Dimitris N. ...