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GPEM
2006
82views more  GPEM 2006»
13 years 5 months ago
Shortcomings with using edge encodings to represent graph structures
There are various representations for encoding graph structures, such as artificial neural networks (ANNs) and circuits, each with its own strengths and weaknesses. Here we analyz...
Gregory Hornby
GECCO
2004
Springer
13 years 10 months ago
Shortcomings with Tree-Structured Edge Encodings for Neural Networks
In evolutionary algorithms a common method for encoding neural networks is to use a tree-structured assembly procedure for constructing them. Since node operators have difficulties...
Gregory Hornby
VIS
2006
IEEE
122views Visualization» more  VIS 2006»
14 years 6 months ago
Representing Higher-Order Singularities in Vector Fields on Piecewise Linear Surfaces
Accurately representing higher-order singularities of vector fields defined on piecewise linear surfaces is a non-trivial problem. In this work, we introduce a concise yet complet...
Wan-Chiu Li, Bruno Vallet, Nicolas Ray, Bruno L...
JMLR
2008
141views more  JMLR 2008»
13 years 5 months ago
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
In graphical modelling, a bi-directed graph encodes marginal independences among random variables that are identified with the vertices of the graph. We show how to transform a bi...
Mathias Drton, Thomas S. Richardson
DAM
2007
105views more  DAM 2007»
13 years 5 months ago
Recognizing and representing proper interval graphs in parallel using merging and sorting
We present a parallel algorithm for recognizing and representing a proper interval graph in O(log2 n) time with O(m + n) processors on the CREW PRAM, where m and n are the number ...
Jørgen Bang-Jensen, Jing Huang, Louis Ibarr...