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» Learning the required number of agents for complex tasks
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VRST
2006
ACM
14 years 1 months ago
Spatial input device structure and bimanual object manipulation in virtual environments
Complex 3D interaction tasks require the manipulation of a large number of input parameters. Spatial input devices can be constructed such that their structure reflects the task ...
Arjen van Rhijn, Jurriaan D. Mulder
BMCBI
2010
109views more  BMCBI 2010»
13 years 8 months ago
Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression
Background: In the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a comple...
Xiaojiang Xu, Stephen Hoang, Marty W. Mayo, Stefan...
CVPR
2012
IEEE
11 years 10 months ago
Sum-product networks for modeling activities with stochastic structure
This paper addresses recognition of human activities with stochastic structure, characterized by variable spacetime arrangements of primitive actions, and conducted by a variable ...
Mohamed R. Amer, Sinisa Todorovic
BC
2002
193views more  BC 2002»
13 years 7 months ago
Resonant spatiotemporal learning in large random recurrent networks
Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives...
Emmanuel Daucé, Mathias Quoy, Bernard Doyon
CVPR
2003
IEEE
14 years 9 months ago
Learning epipolar geometry from image sequences
We wish to determine the epipolar geometry of a stereo camera pair from image measurements alone. This paper describes a solution to this problem which does not require a parametr...
Yonatan Wexler, Andrew W. Fitzgibbon, Andrew Zisse...