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ICML
2010
IEEE
15 years 5 months ago
Bottom-Up Learning of Markov Network Structure
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...
Jesse Davis, Pedro Domingos
JVCA
2006
87views more  JVCA 2006»
15 years 4 months ago
Interactive mesh deformation with pseudo material effects
This paper presents a novel interactive mesh deformation method that can achieve various dynamic material effects, including elastic membrane and cloth effects. In our framework, ...
Jin Huang, Hongxin Zhang, Xiaohan Shi, Xinguo Liu,...
AML
2005
65views more  AML 2005»
15 years 4 months ago
Axiomatization of local-global principles for pp-formulas in spaces of orderings
es of orderings (an abstract version of real spectras of formally real fields), for which they are expressed as local-global principles: A property of quadratic forms (expressed as...
Vincent Astier, Marcus Tressl
TCSV
2008
120views more  TCSV 2008»
15 years 4 months ago
A Parallel Hardware Architecture for Scale and Rotation Invariant Feature Detection
Abstract--This paper proposes a parallel hardware architecture for image feature detection based on the SIFT (Scale Invariant Feature Transform) algorithm and applied to the SLAM (...
Vanderlei Bonato, Eduardo Marques, George A. Const...
FUIN
2002
63views more  FUIN 2002»
15 years 4 months ago
Probabilistic Cluster Unfoldings
Abstract. This article introduces probabilistic cluster branching processes, a probabilistic unfolding semantics for untimed Petri nets, with no structural or safety assumptions, g...
Stefan Haar