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» Learning the Structure of Dynamic Probabilistic Networks
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GLOBECOM
2010
IEEE
13 years 5 months ago
Cognitive Network Inference through Bayesian Network Analysis
Cognitive networking deals with applying cognition to the entire network protocol stack for achieving stack-wide as well as network-wide performance goals, unlike cognitive radios ...
Giorgio Quer, Hemanth Meenakshisundaram, Tamma Bhe...
FCCM
2000
IEEE
103views VLSI» more  FCCM 2000»
14 years 1 days ago
A Networked FPGA-Based Hardware Implementation of a Neural Network Application
This paper describes a networked FPGA-based implementation of the FAST (Flexible Adaptable-Size Topology) architecture, a Arti cial Neural Network (ANN) that dynamically adapts it...
Héctor Fabio Restrepo, Ralph Hoffmann, Andr...
BMCBI
2006
239views more  BMCBI 2006»
13 years 7 months ago
Applying dynamic Bayesian networks to perturbed gene expression data
Background: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics,...
Norbert Dojer, Anna Gambin, Andrzej Mizera, Bartek...
ICCV
2007
IEEE
13 years 9 months ago
COST: An Approach for Camera Selection and Multi-Object Inference Ordering in Dynamic Scenes
Development of multiple camera based vision systems for analysis of dynamic objects such as humans is challenging due to occlusions and similarity in the appearance of a person wi...
Abhinav Gupta, Anurag Mittal, Larry S. Davis
NIPS
2004
13 years 9 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra