Add to ORKGThe paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradient descent learning rules to train their equilibrium solutions. A theorem is given that specifies sufficient conditions for the gradient descent learning rules to be local covariance statistics between two random variables: 1) an evaluator which is the same for all the network parameters, and 2) a system variable which is independent of the learning objective. The generality of the theorem suggests that instead of suppressing noise present in physical devices, a natural alternative is to use it to simplify the credit assignment problem. In deterministic networks credit assignment requires an evaluation signal which is different for each node ...
Machine learning, and in particular neural network models, have revolutionized fields such as image,...
Leveraging advances in variational inference, we propose to enhance recurrent neural networks with l...
It has been hypothesized that neural network models with cyclic connectivity may be more powerful th...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
Many connectionist learning algorithms consists of minimizing a cost of the form C(w) = E(J(z; w)) ...
The basic structure and definitions of artificial neural networks are exposed, as an introduction to...
Introduction The work reported here began with the desire to find a network architecture that shared...
Training neural networks with discrete stochastic variables presents a unique challenge. Backpropaga...
Abstract—We have found a more general formulation of the REINFORCE learning principle which had been...
We introduce the stochastic gradient descent algorithm used in the computational network toolkit (CN...
We study on-line gradient-descent learning in multilayer networks analytically and numerically. The ...
We have found a more general formulation of the REINFORCE learning principle which had been proposed...
The deep learning optimization community has observed how the neural networks generalization ability...
The first purpose of this paper is to present a class of algorithms for finding the global minimum o...
Numerous models for supervised and reinforcement learning benefit from combinations of discrete and ...
Machine learning, and in particular neural network models, have revolutionized fields such as image,...
Leveraging advances in variational inference, we propose to enhance recurrent neural networks with l...
It has been hypothesized that neural network models with cyclic connectivity may be more powerful th...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
Many connectionist learning algorithms consists of minimizing a cost of the form C(w) = E(J(z; w)) ...
The basic structure and definitions of artificial neural networks are exposed, as an introduction to...
Introduction The work reported here began with the desire to find a network architecture that shared...
Training neural networks with discrete stochastic variables presents a unique challenge. Backpropaga...
Abstract—We have found a more general formulation of the REINFORCE learning principle which had been...
We introduce the stochastic gradient descent algorithm used in the computational network toolkit (CN...
We study on-line gradient-descent learning in multilayer networks analytically and numerically. The ...
We have found a more general formulation of the REINFORCE learning principle which had been proposed...
The deep learning optimization community has observed how the neural networks generalization ability...
The first purpose of this paper is to present a class of algorithms for finding the global minimum o...
Numerous models for supervised and reinforcement learning benefit from combinations of discrete and ...
Machine learning, and in particular neural network models, have revolutionized fields such as image,...
Leveraging advances in variational inference, we propose to enhance recurrent neural networks with l...
It has been hypothesized that neural network models with cyclic connectivity may be more powerful th...