Abstract—In this paper, the natural gradient descent method for the multilayer stochastic complex-valued neural networks is considered, and the natural gradient is given for a single stochastic complex-valued neuron as an example. Since the space of the learnable parameters of stochastic complex-valued neural networks is not the Euclidean space but a curved manifold, the complex-valued natural gradient method is expected to exhibit excellent learning performance. Keywords—Neural network; Complex number; Learning; Sin-gular poin
We analyse natural gradient learning in a two-layer feed-forward neural network using a statistical ...
Natural gradient descent (NGD) is an on-line algorithm for redefining the steepest descent direction...
A multilayer neural network based on multi-valued neurons is considered in the paper. A multivalued ...
The parameter space of neural networks has the Riemannian metric structure. The natural Riemannian g...
Recent advancements in the field of telecommunications, medical imaging and signal processing deal w...
An instance of artificial neural learning is by criterion optimization, where the criterion to optim...
This report details the conception, design , implementation and analysis through comparative testing...
A complex valued neural network is a neural network which consists of complex valued input and/or we...
Recent developments in complex-valued feed-forward neural networks have found number of applications...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
The natural gradient descent method is applied to train an n-m-1 mul-tilayer perceptron. Based on an...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
Machine learning, and in particular neural network models, have revolutionized fields such as image,...
Stochastic gradient descent (SGD) remains the method of choice for deep learning, despite the limita...
Complex-valued data arise in various applications, such as radar and array signal processing, magne...
We analyse natural gradient learning in a two-layer feed-forward neural network using a statistical ...
Natural gradient descent (NGD) is an on-line algorithm for redefining the steepest descent direction...
A multilayer neural network based on multi-valued neurons is considered in the paper. A multivalued ...
The parameter space of neural networks has the Riemannian metric structure. The natural Riemannian g...
Recent advancements in the field of telecommunications, medical imaging and signal processing deal w...
An instance of artificial neural learning is by criterion optimization, where the criterion to optim...
This report details the conception, design , implementation and analysis through comparative testing...
A complex valued neural network is a neural network which consists of complex valued input and/or we...
Recent developments in complex-valued feed-forward neural networks have found number of applications...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
The natural gradient descent method is applied to train an n-m-1 mul-tilayer perceptron. Based on an...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
Machine learning, and in particular neural network models, have revolutionized fields such as image,...
Stochastic gradient descent (SGD) remains the method of choice for deep learning, despite the limita...
Complex-valued data arise in various applications, such as radar and array signal processing, magne...
We analyse natural gradient learning in a two-layer feed-forward neural network using a statistical ...
Natural gradient descent (NGD) is an on-line algorithm for redefining the steepest descent direction...
A multilayer neural network based on multi-valued neurons is considered in the paper. A multivalued ...