Add to ORKGThe first purpose of this paper is to present a class of algorithms for finding the global minimum of a continuous-variable function defined on a hypercube. These algorithms, based on both diffusion processes and simulated annealing, are implementable as analog integrated circuits. Such circuits can be viewed as generalizations of neural networks of the Hopfield type, and are called "diffusion machines." Our second objective isto show that "learning " in these networks can be achieved by a set of three interconnected diffusion machines: one that learns, one to model the desired behavior, and one to compute the weight changes
This paper proposes a new family of algorithms for training neural networks (NNs). These...
This paper addresses the problem of neural computing by a fundamentally different approach to the on...
We study the overparametrization bounds required for the global convergence of stochastic gradient d...
We investigate a novel neural network model which uses stochastic weights. It is shown that the func...
Many connectionist learning algorithms consists of minimizing a cost of the form C(w) = E(J(z; w)) ...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
Introduction The work reported here began with the desire to find a network architecture that shared...
This paper reviews some of the recent results in applying the theory of Probably Approximately Corre...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
[[abstract]]The Diffusion Network (DN) is a probabilistic model capable of recognising continuous-ti...
Artificial (or biological) Neural Networks must be able to form by learning internal memory of the e...
Complex diagnosis problems, defined by high-level models, often lead to constraint-based discrete op...
The basic structure and definitions of artificial neural networks are exposed, as an introduction to...
This paper presents a new stochastic learning algorithm suitable for analog implementation. The Neur...
AbstractNeural information processing models largely assume that the patterns for training a neural ...
This paper proposes a new family of algorithms for training neural networks (NNs). These...
This paper addresses the problem of neural computing by a fundamentally different approach to the on...
We study the overparametrization bounds required for the global convergence of stochastic gradient d...
We investigate a novel neural network model which uses stochastic weights. It is shown that the func...
Many connectionist learning algorithms consists of minimizing a cost of the form C(w) = E(J(z; w)) ...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
Introduction The work reported here began with the desire to find a network architecture that shared...
This paper reviews some of the recent results in applying the theory of Probably Approximately Corre...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
[[abstract]]The Diffusion Network (DN) is a probabilistic model capable of recognising continuous-ti...
Artificial (or biological) Neural Networks must be able to form by learning internal memory of the e...
Complex diagnosis problems, defined by high-level models, often lead to constraint-based discrete op...
The basic structure and definitions of artificial neural networks are exposed, as an introduction to...
This paper presents a new stochastic learning algorithm suitable for analog implementation. The Neur...
AbstractNeural information processing models largely assume that the patterns for training a neural ...
This paper proposes a new family of algorithms for training neural networks (NNs). These...
This paper addresses the problem of neural computing by a fundamentally different approach to the on...
We study the overparametrization bounds required for the global convergence of stochastic gradient d...