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....
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...
—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...
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...
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...