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ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 6 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
CORR
2010
Springer
207views Education» more  CORR 2010»
15 years 3 months ago
TILT: Transform Invariant Low-rank Textures
Abstract. In this paper, we show how to efficiently and effectively extract a rich class of low-rank textures in a 3D scene from 2D images despite significant distortion and warpin...
Zhengdong Zhang, Arvind Ganesh, Xiao Liang, Yi Ma
TIP
2011
255views more  TIP 2011»
15 years 1 months ago
Dictionary Learning for Stereo Image Representation
—One of the major challenges in multi-view imaging is the definition of a representation that reveals the intrinsic geometry of the visual information. Sparse image representati...
Ivana Tosic, Pascal Frossard
JCNS
2010
90views more  JCNS 2010»
15 years 1 months ago
Fast Kalman filtering on quasilinear dendritic trees
Optimal filtering of noisy voltage signals on dendritic trees is a key problem in computational cellular neuroscience. However, the state variable in this problem -- the vector of...
Liam Paninski
TSP
2010
15 years 1 months ago
Block-sparse signals: uncertainty relations and efficient recovery
We consider efficient methods for the recovery of block-sparse signals--i.e., sparse signals that have nonzero entries occurring in clusters--from an underdetermined system of line...
Yonina C. Eldar, Patrick Kuppinger, Helmut Bö...