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ESANN
2003
13 years 9 months ago
Autonomous learning algorithm for fully connected recurrent networks
In this paper fully connected RTRL neural networks are studied. In order to learn dynamical behaviours of linear-processes or to predict time series, an autonomous learning algori...
Edouard Leclercq, Fabrice Druaux, Dimitri Lefebvre
IAT
2008
IEEE
13 years 8 months ago
Scaling Up Multi-agent Reinforcement Learning in Complex Domains
TD-FALCON (Temporal Difference - Fusion Architecture for Learning, COgnition, and Navigation) is a class of self-organizing neural networks that incorporates Temporal Difference (...
Dan Xiao, Ah-Hwee Tan
ICANN
2005
Springer
14 years 1 months ago
A Neural Network Model for Inter-problem Adaptive Online Time Allocation
One aim of Meta-learning techniques is to minimize the time needed for problem solving, and the effort of parameter hand-tuning, by automating algorithm selection. The predictive m...
Matteo Gagliolo, Jürgen Schmidhuber
IJCNN
2006
IEEE
14 years 1 months ago
Event modeling of message interchange in stochastic neural ensembles
— We propose a modeling framework based on the event-driven paradigm for populations of neurons which interchange messages. Unlike other strategies our approach is focused on the...
Vicenç Gómez, Andreas Kaltenbrunner,...
FGCN
2008
IEEE
236views Communications» more  FGCN 2008»
14 years 2 months ago
Dimensioning of Next Generation Networks Signaling Gateway for Improving a Quality of Service Target
In this paper, we propose a set of dimensioning rules, which deliver high quality sessionbased services over a Next Generation Network based IP/MPLS transport infrastructure. In p...
Cherif Ghazel, Leila Saidane