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NIPS
2007
13 years 11 months ago
Discovering Weakly-Interacting Factors in a Complex Stochastic Process
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Charlie Frogner, Avi Pfeffer
NIPS
2001
13 years 11 months ago
Unsupervised Learning of Human Motion Models
This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatic...
Yang Song, Luis Goncalves, Pietro Perona
CORR
2008
Springer
108views Education» more  CORR 2008»
13 years 10 months ago
Resolution Trees with Lemmas: Resolution Refinements that Characterize DLL Algorithms with Clause Learning
Resolution refinements called w-resolution trees with lemmas (WRTL) and with input lemmas (WRTI) are introduced. Dag-like resolution is equivalent to both WRTL and WRTI when there...
Samuel R. Buss, Jan Hoffmann 0002, Jan Johannsen
AI
2006
Springer
13 years 10 months ago
Propagation algorithms for lexicographic ordering constraints
Finite-domain constraint programming has been used with great success to tackle a wide variety of combinatorial problems in industry and academia. To apply finite-domain constrain...
Alan M. Frisch, Brahim Hnich, Zeynep Kiziltan, Ian...
JMLR
2006
103views more  JMLR 2006»
13 years 10 months ago
MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals
In recent years, there has been a growing interest in applying Bayesian networks and their extensions to reconstruct regulatory networks from gene expression data. Since the gene ...
Dana Pe'er, Amos Tanay, Aviv Regev