Add to ORKGIn this paper, a linear approximation for Gelenbe's Learning Algorithm developed for training Recurrent Random Neural Networks (RRNN) is proposed. Gelenbe's learning algorithm uses gradient descent of a quadratic error function in which the main computational effort is for obtaining the inverse of an n-by-n matrix. In this paper, the inverse of this matrix is approximated with a linear term and the efficiency of the approximated algorithm is examined when RRNN is trained as autoassociative memory
In this work a novel approach to the training of recurrent neural nets is presented. the algorithm e...
Random Neural Networks (RNNs) area classof Neural Networks (NNs) that can also be seen as a specific...
A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonline...
The random neural network model proposed by Gelenbe has a number of interesting features in addition...
A real time recurrent learning (RTRL) algorithm with an adaptive-learning rate for nonlinear adaptiv...
Learning is one of the most important useful features of artificial neural networks. In engineering ...
In this work a novel approach to the training of recurrent neural nets is presented. The algorithm e...
This paper presents a new approach to learning in recurrent neural networks, based on the descent of...
Learning is one of the most important useful features of artificial neural networks. In engineering ...
Training of recurrent neural networks (RNNs) introduces considerable computational complexities due ...
Training of recurrent neural networks (RNNs) introduces considerable computational complexities due ...
We propose a pre-training technique for recurrent neural networks based on linear autoencoder networ...
In this chapter, we describe the basic concepts behind the functioning of recurrent neural networks ...
Recurrent neural networks have the potential to perform significantly better than the commonly used ...
Learning to solve sequential tasks with recurrent models requires the ability to memorize long seque...
In this work a novel approach to the training of recurrent neural nets is presented. the algorithm e...
Random Neural Networks (RNNs) area classof Neural Networks (NNs) that can also be seen as a specific...
A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonline...
The random neural network model proposed by Gelenbe has a number of interesting features in addition...
A real time recurrent learning (RTRL) algorithm with an adaptive-learning rate for nonlinear adaptiv...
Learning is one of the most important useful features of artificial neural networks. In engineering ...
In this work a novel approach to the training of recurrent neural nets is presented. The algorithm e...
This paper presents a new approach to learning in recurrent neural networks, based on the descent of...
Learning is one of the most important useful features of artificial neural networks. In engineering ...
Training of recurrent neural networks (RNNs) introduces considerable computational complexities due ...
Training of recurrent neural networks (RNNs) introduces considerable computational complexities due ...
We propose a pre-training technique for recurrent neural networks based on linear autoencoder networ...
In this chapter, we describe the basic concepts behind the functioning of recurrent neural networks ...
Recurrent neural networks have the potential to perform significantly better than the commonly used ...
Learning to solve sequential tasks with recurrent models requires the ability to memorize long seque...
In this work a novel approach to the training of recurrent neural nets is presented. the algorithm e...
Random Neural Networks (RNNs) area classof Neural Networks (NNs) that can also be seen as a specific...
A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonline...