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NECO
2007
127views more  NECO 2007»
13 years 6 months ago
Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...
Joseph F. Murray, Kenneth Kreutz-Delgado
CORR
2010
Springer
253views Education» more  CORR 2010»
13 years 7 months ago
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of t...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Yann LeCu...
TMI
2008
138views more  TMI 2008»
13 years 6 months ago
Dynamic Positron Emission Tomography Data-Driven Analysis Using Sparse Bayesian Learning
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework usin...
Jyh-Ying Peng, John A. D. Aston, R. N. Gunn, Cheng...
CGVR
2009
13 years 5 months ago
Interactive Models From Images of a Static Scene
FXPAL's Pantheia system enables users to create virtual models by `marking up' a physical space with pre-printed visual markers. The meanings associated with the markers...
Eleanor G. Rieffel, Don Kimber, Jim Vaughan, Sagar...
UAI
2008
13 years 8 months ago
Projected Subgradient Methods for Learning Sparse Gaussians
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
John Duchi, Stephen Gould, Daphne Koller