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TSP
2010
13 years 3 months ago
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
ACL
1990
13 years 10 months ago
Structural Disambiguation with Constraint Propagation
We present a new grammatical formalism called Constraint Dependency Grammar (CDG) in which every grammatical rule is given as a constraint on wordto-word modifications. CDG parsin...
Hiroshi Maruyama
KDD
1999
ACM
152views Data Mining» more  KDD 1999»
14 years 1 months ago
Applying General Bayesian Techniques to Improve TAN Induction
Tree Augmented Naive Bayes (TAN) has shown to be competitive with state-of-the-art machine learning algorithms [3]. However, the TAN induction algorithm that appears in [3] can be...
Jesús Cerquides
ISPD
2005
ACM
130views Hardware» more  ISPD 2005»
14 years 2 months ago
Improved algorithms for link-based non-tree clock networks for skew variability reduction
In the nanometer VLSI technology, the variation effects like manufacturing variation, power supply noise, temperature etc. become very significant. As one of the most vital nets...
Anand Rajaram, David Z. Pan, Jiang Hu
AI
2000
Springer
13 years 9 months ago
Stochastic dynamic programming with factored representations
Markov decisionprocesses(MDPs) haveproven to be popular models for decision-theoretic planning, but standard dynamic programming algorithms for solving MDPs rely on explicit, stat...
Craig Boutilier, Richard Dearden, Moisés Go...