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» Using Gaussian Processes to Optimize Expensive Functions
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TSP
2008
179views more  TSP 2008»
13 years 7 months ago
Estimation in Gaussian Graphical Models Using Tractable Subgraphs: A Walk-Sum Analysis
Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fie...
V. Chandrasekaran, Jason K. Johnson, Alan S. Wills...
NIPS
2004
13 years 9 months ago
Joint Tracking of Pose, Expression, and Texture using Conditionally Gaussian Filters
We present a generative model and stochastic filtering algorithm for simultaneous tracking of 3D position and orientation, non-rigid motion, object texture, and background texture...
Tim K. Marks, John R. Hershey, J. Cooper Roddey, J...
CEC
2007
IEEE
14 years 1 months ago
Non-separable fitness functions for evolutionary shape optimization benchmarking
—Target shape matching can be used as a quick and easy surrogate task when evaluating optimization algorithms intended for computationally expensive tasks, such as turbine blade ...
Tim A. Yates, Thorsten Schnier
PLDI
2000
ACM
13 years 12 months ago
Caching function calls using precise dependencies
This paper describes the implementation of a purely functional programming language for building software systems. In this language, external tools like compilers and linkers are ...
Allan Heydon, Roy Levin, Yuan Yu
GECCO
2008
Springer
177views Optimization» more  GECCO 2008»
13 years 8 months ago
Reduced computation for evolutionary optimization in noisy environment
Evolutionary Algorithms’ (EAs’) application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on ...
Maumita Bhattacharya