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» TRUST-TECH Based Neural Network Training
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IJCNN
2008
IEEE
10 years 10 months ago
Biologically realizable reward-modulated hebbian training for spiking neural networks
— Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximat...
Silvia Ferrari, Bhavesh Mehta, Gianluca Di Muro, A...
MCS
2006
Springer
10 years 4 months ago
Variable projections neural network training
8 The training of some types of neural networks leads to separable non-linear least squares problems. These problems may be9 ill-conditioned and require special techniques. A robus...
V. Pereyra, G. Scherer, F. Wong
ICANN
2010
Springer
10 years 5 months ago
Reinforcement Learning Based Neural Controllers for Dynamic Processes without Exploration
Abstract. In this paper we present a Reinforcement Learning (RL) approach with the capability to train neural adaptive controllers for complex control problems without expensive on...
Frank-Florian Steege, André Hartmann, Erik ...
TNN
2008
177views more  TNN 2008»
10 years 4 months ago
Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting...
Yoshua Bengio, Jean-Sébastien Senecal
IJON
2006
111views more  IJON 2006»
10 years 4 months ago
Dynamic pruning algorithm for multilayer perceptron based neural control systems
Generalization ability of neural networks is very important and a rule of thumb for good generalization in neural systems is that the smallest system should be used to fit the tra...
Jie Ni, Qing Song
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