Add to ORKGWe analyse natural gradient learning in a two-layer feed-forward neural network using a statistical mechanics framework which is appropriate for large input di-mension. We find significant improvement over standard gradient descent in both the transient and asymptotic phases of learning.
This paper deals with studying the asymptotical properties of multilayer neural networks models used...
Stochastic gradient descent (SGD) remains the method of choice for deep learning, despite the limita...
Natural gradient learning is an efficient and principled method for improving on-line learning. In p...
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...
Natural gradient learning is an efficient and principled method for improving online learning. In pr...
In this paper we investigate the application of natural gradient descent to Bellman error based rein...
We analyse the dynamics of a number of second order on-line learning algorithms training multi-layer...
When a parameter space has a certain underlying structure, the ordinary gradient of a function does ...
tems, quantum biology, and relevant aspects of thermodynamics, information theory, cybernetics, and ...
We study on-line gradient-descent learning in multilayer networks analytically and numerically. The ...
We complement recent advances in thermodynamic limit analyses of mean on-line gradient descent learn...
International audienceDeep neural networks achieve stellar generalisation even when they have enough...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
This paper applies natural gradient (NG) learning neural networks (NNs) for modeling and identificat...
This paper deals with studying the asymptotical properties of multilayer neural networks models used...
Stochastic gradient descent (SGD) remains the method of choice for deep learning, despite the limita...
Natural gradient learning is an efficient and principled method for improving on-line learning. In p...
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...
Natural gradient learning is an efficient and principled method for improving online learning. In pr...
In this paper we investigate the application of natural gradient descent to Bellman error based rein...
We analyse the dynamics of a number of second order on-line learning algorithms training multi-layer...
When a parameter space has a certain underlying structure, the ordinary gradient of a function does ...
tems, quantum biology, and relevant aspects of thermodynamics, information theory, cybernetics, and ...
We study on-line gradient-descent learning in multilayer networks analytically and numerically. The ...
We complement recent advances in thermodynamic limit analyses of mean on-line gradient descent learn...
International audienceDeep neural networks achieve stellar generalisation even when they have enough...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
This paper applies natural gradient (NG) learning neural networks (NNs) for modeling and identificat...
This paper deals with studying the asymptotical properties of multilayer neural networks models used...
Stochastic gradient descent (SGD) remains the method of choice for deep learning, despite the limita...
Natural gradient learning is an efficient and principled method for improving on-line learning. In p...