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UAI
1996
15 years 5 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
131
Voted
ICML
2010
IEEE
15 years 5 months ago
Learning Markov Logic Networks Using Structural Motifs
Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extre...
Stanley Kok, Pedro Domingos
INTERNET
2006
119views more  INTERNET 2006»
15 years 4 months ago
Toward a Grid-Based DBMS
level is a grid abstract machine, which consists of a collection of typed nodes -- data nodes (purple), director nodes (red), management nodes (orange), and various processing node...
Craig W. Thompson
TSE
2008
129views more  TSE 2008»
15 years 3 months ago
Classifying Software Changes: Clean or Buggy?
This paper introduces a new technique for predicting latent software bugs, called change classification. Change classification uses a machine learning classifier to determine wheth...
Sunghun Kim, E. James Whitehead Jr., Yi Zhang 0001
MVA
2010
229views Computer Vision» more  MVA 2010»
14 years 10 months ago
Robust 3D object registration without explicit correspondence using geometric integration
3D vision guided manipulation of components is a key problem of industrial machine vision. In this paper, we focus on the localization and pose estimation of known industrial objec...
Dirk Breitenreicher, Christoph Schnörr