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CVPR
2008
IEEE
16 years 3 months ago
The Logistic Random Field - A convenient graphical model for learning parameters for MRF-based labeling
Graphical models are fundamental tools for modeling images and other applications. In this paper, we propose the Logistic Random Field (LRF) model for representing a discrete-valu...
Marshall F. Tappen, Kegan G. G. Samuel, Craig V. D...
DSVIS
2005
Springer
15 years 7 months ago
A Method for the Verification of Haptic Algorithms
Joan De Boeck, Chris Raymaekers, Karin Coninx
GECCO
2008
Springer
257views Optimization» more  GECCO 2008»
15 years 3 months ago
Rapid evaluation and evolution of neural models using graphics card hardware
This paper compares three common evolutionary algorithms and our modified GA, a Distributed Adaptive Genetic Algorithm (DAGA). The optimal approach is sought to adapt, in near rea...
Thomas F. Clayton, Leena N. Patel, Gareth Leng, Al...
ICASSP
2011
IEEE
14 years 5 months ago
Scalable robust hypothesis tests using graphical models
Traditional binary hypothesis testing relies on the precise knowledge of the probability density of an observed random vector conditioned on each hypothesis. However, for many app...
Divyanshu Vats, Vishal Monga, Umamahesh Srinivas, ...
UAI
2001
15 years 3 months ago
Graphical Models for Game Theory
We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and efficient algorithm for computing approximate Nash equ...
Michael J. Kearns, Michael L. Littman, Satinder P....