Add to ORKGNeural networks are well known as universal approximators. But the performance of neural network depends on several parameters. Finding the best set of parameters has no theoretical answer. Neural network users are forced to try different sets of parameters, and then, choose the best among them. In this paper, we present a parallel solution, based on a master-slave software architecture. Tests are reported on two platforms (workstations network and Cray T3D), using the PVM environment. The load-balancing problem is also addressed. Results are given, for the approximation and the speed-up of the parallel execution.Les réseaux de neurones constituent des approximateurs universels. Mais leurs performances dépendent de plusieurs paramètres dont...
The generalization performance of deep neural networks comes from their ability to learn, which requ...
Les algorithmes d’apprentissage automatique utilisant des réseaux de neurones profonds ont récemment...
(eng) We present a general model for differentiable feed-forward neural networks. Its general mathem...
Neural networks are well known as universal approximators. But the performance of neural network dep...
Cette dernière décennie a donné lieu à la réémergence des méthodes d'apprentissage machine basées su...
artificial neural networks; CNN network; cellular neural network (CNN); learning; optimisation; arti...
As artificial neural networks (ANNs) gain popularity in a variety of application domains, it is crit...
. The OWE (Orthogonal Weight Estimator) architecture is constituted of a main MLP in which the value...
It seems to be an everlasting discussion. Spending a lot of additional time and extra money to imple...
Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face...
Many error resilient applications can be approximated using multi-layer perceptrons (MLPs) with insi...
The training phase in Deep Neural Networks has become an important source of computing resource usag...
La performance de généralisation des réseaux de neurones profonds vient de leur capacité d'apprentis...
The structure of a neural network determines to a large extent its cost of training and use, as well...
International audienceLes Réseaux de Neurones Profonds (RNPs) reposent sur un grand nombre de paramè...
The generalization performance of deep neural networks comes from their ability to learn, which requ...
Les algorithmes d’apprentissage automatique utilisant des réseaux de neurones profonds ont récemment...
(eng) We present a general model for differentiable feed-forward neural networks. Its general mathem...
Neural networks are well known as universal approximators. But the performance of neural network dep...
Cette dernière décennie a donné lieu à la réémergence des méthodes d'apprentissage machine basées su...
artificial neural networks; CNN network; cellular neural network (CNN); learning; optimisation; arti...
As artificial neural networks (ANNs) gain popularity in a variety of application domains, it is crit...
. The OWE (Orthogonal Weight Estimator) architecture is constituted of a main MLP in which the value...
It seems to be an everlasting discussion. Spending a lot of additional time and extra money to imple...
Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face...
Many error resilient applications can be approximated using multi-layer perceptrons (MLPs) with insi...
The training phase in Deep Neural Networks has become an important source of computing resource usag...
La performance de généralisation des réseaux de neurones profonds vient de leur capacité d'apprentis...
The structure of a neural network determines to a large extent its cost of training and use, as well...
International audienceLes Réseaux de Neurones Profonds (RNPs) reposent sur un grand nombre de paramè...
The generalization performance of deep neural networks comes from their ability to learn, which requ...
Les algorithmes d’apprentissage automatique utilisant des réseaux de neurones profonds ont récemment...
(eng) We present a general model for differentiable feed-forward neural networks. Its general mathem...