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ESANN
2003
15 years 1 months ago
Robust Topology Representing Networks
Martinetz and Schulten proposed the use of a Competitive Hebbian Learning (CHL) rule to build Topology Representing Networks. From a set of units and a data distribution, a link i...
Michaël Aupetit
AROBOTS
2011
14 years 6 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox
JAIR
2008
93views more  JAIR 2008»
14 years 11 months ago
A Rigorously Bayesian Beam Model and an Adaptive Full Scan Model for Range Finders in Dynamic Environments
This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. All modeling assumptions are rigorously explained, and...
Tinne De Laet, Joris De Schutter, Herman Bruyninck...
ICCV
2007
IEEE
16 years 1 months ago
Learning Higher-order Transition Models in Medium-scale Camera Networks
We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the...
Ryan Farrell, David S. Doermann, Larry S. Davis
LICS
2010
IEEE
14 years 10 months ago
Abstracting the Differential Semantics of Rule-Based Models: Exact and Automated Model Reduction
ing the differential semantics of rule-based models: exact and automated model reduction (Invited Lecture) Vincent Danos∗§, J´erˆome Feret†, Walter Fontana‡, Russell Harme...
Vincent Danos, Jérôme Feret, Walter F...