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» Using Gaussian Processes to Optimize Expensive Functions
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IROS
2006
IEEE
124views Robotics» more  IROS 2006»
15 years 7 months ago
Simulation of Acquisition of Locomotion of an Infant Robot
Abstract— The authors proposed a simulation method to clarify the developmental process of human locomotion using a model of infant robot. The infant robot has many degrees of fr...
Katsuyoshi Tsujita, Tatsuya Masuda
AIPS
2008
15 years 3 months ago
Learning Relational Decision Trees for Guiding Heuristic Planning
The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computat...
Tomás de la Rosa, Sergio Jiménez, Da...
CVPR
2012
IEEE
13 years 3 months ago
Weakly supervised structured output learning for semantic segmentation
We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test im...
Alexander Vezhnevets, Vittorio Ferrari, Joachim M....
GECCO
2006
Springer
206views Optimization» more  GECCO 2006»
15 years 4 months ago
A dynamically constrained genetic algorithm for hardware-software partitioning
In this article, we describe the application of an enhanced genetic algorithm to the problem of hardware-software codesign. Starting from a source code written in a high-level lan...
Pierre-André Mudry, Guillaume Zufferey, Gia...
CP
2010
Springer
14 years 11 months ago
Towards Parallel Non Serial Dynamic Programming for Solving Hard Weighted CSP
We introduce a parallelized version of tree-decomposition based dynamic programming for solving difficult weighted CSP instances on many cores. A tree decomposition organizes cost ...
David Allouche, Simon de Givry, Thomas Schiex