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» Methods to Learn Abstract Scheduling Models
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IJCV
2010
158views more  IJCV 2010»
14 years 8 months ago
Metric Learning for Image Alignment
Abstract Image alignment has been a long standing problem in computer vision. Parameterized Appearance Models (PAMs) such as the Lucas-Kanade method, Eigentracking, and Active Appe...
Minh Hoai Nguyen, Fernando De la Torre
SCALESPACE
2009
Springer
15 years 4 months ago
Momentum Based Optimization Methods for Level Set Segmentation
Abstract. Segmentation of images is often posed as a variational problem. As such, it is solved by formulating an energy functional depending on a contour and other image derived t...
Gunnar Läthén, Thord Andersson, Reiner...
GECCO
2005
Springer
166views Optimization» more  GECCO 2005»
15 years 3 months ago
The emulation of social institutions as a method of coevolution
This paper offers a novel approach to coevolution based on the sociological theory of symbolic interactionism. It provides a multi-agent computational model along with experimenta...
Deborah Vakas Duong, John J. Grefenstette
JMLR
2010
141views more  JMLR 2010»
14 years 4 months ago
FastInf: An Efficient Approximate Inference Library
The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is pro...
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elida...
AUSAI
2006
Springer
15 years 1 months ago
Learning Hybrid Bayesian Networks by MML
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditi...
Rodney T. O'Donnell, Lloyd Allison, Kevin B. Korb