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JMLR
2010
137views more  JMLR 2010»
14 years 9 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
120
Voted
ICANN
2010
Springer
15 years 3 months ago
Exploring Continuous Action Spaces with Diffusion Trees for Reinforcement Learning
We propose a new approach for reinforcement learning in problems with continuous actions. Actions are sampled by means of a diffusion tree, which generates samples in the continuou...
Christian Vollmer, Erik Schaffernicht, Horst-Micha...
141
Voted
ICIP
2004
IEEE
16 years 3 months ago
Optimized space sampling for circular image cube trajectory analysis
Image Cube Trajectory (ICT) analysis is a new and robust method to estimate the 3D structure of a scene from a set of 2D images. The motion of points in 3D space is represented by...
Ingo Feldmann, Peter Kauff, Peter Eisert
90
Voted
ICPR
2008
IEEE
16 years 3 months ago
Fast protein homology and fold detection with sparse spatial sample kernels
In this work we present a new string similarity feature, the sparse spatial sample (SSS). An SSS is a set of short substrings at specific spatial displacements contained in the or...
Pai-Hsi Huang, Pavel P. Kuksa, Vladimir Pavlovic
126
Voted
CGF
2010
153views more  CGF 2010»
15 years 2 months ago
Localized Delaunay Refinement for Sampling and Meshing
The technique of Delaunay refinement has been recognized as a versatile tool to generate Delaunay meshes of a variety of geometries. Despite its usefulness, it suffers from one la...
Tamal K. Dey, Joshua A. Levine, A. Slatton