Add to ORKGWe consider the mean field theory of optimally pruned perceptrons. Using the cavity method, microscopic equations for the weights and the examples are derived. Their statistical properties agree with previous results using the replica method. There is a gap in the weight distribution, causing an instability in the ground state. A rough energy landscape better describes the learning problem. Solutions to the microscopic equations result in high stability of the examples
The cerebellum is a brain structure which has been traditionally devoted to supervised learning. Acc...
International audienceIn this paper we consider a measure-theoretical formulation of the training of...
Abstract We consider the generalization problem for a perceptron with binary synapses, implementing ...
. We consider the mean field theory of optimally pruned perceptrons. Using the cavity method, micros...
We consider the microscopic equations for learning problems in neural networks. The aligning fields ...
Using the cavity method, I derive the microscopic equations and their stability condition for inform...
Perceptrons are the building blocks of many theoretical approaches to a wide range of complex system...
We investigate the learning of a rule from examples of the case of boolean perceptron. Previous stud...
Learning algorithms for perceptrons are deduced from statistical mechanics. Thermodynamical quantiti...
Within a Kuhn-Tucker cavity method introduced in a former paper, we study optimal stability learning...
Various applications of the mean field theory (MFT) technique for obtaining solutions close to optim...
An input-output map in which the patterns are divided into classes is considered for the perceptron....
Learning Markov random field (MRF) models is notoriously hard due to the presence of a global norm...
Zero temperature Gibbs learning is considered for a connected committee machine with K hidden units....
We study two different unsupervised learning strategies for a single-layer perceptron. The environme...
The cerebellum is a brain structure which has been traditionally devoted to supervised learning. Acc...
International audienceIn this paper we consider a measure-theoretical formulation of the training of...
Abstract We consider the generalization problem for a perceptron with binary synapses, implementing ...
. We consider the mean field theory of optimally pruned perceptrons. Using the cavity method, micros...
We consider the microscopic equations for learning problems in neural networks. The aligning fields ...
Using the cavity method, I derive the microscopic equations and their stability condition for inform...
Perceptrons are the building blocks of many theoretical approaches to a wide range of complex system...
We investigate the learning of a rule from examples of the case of boolean perceptron. Previous stud...
Learning algorithms for perceptrons are deduced from statistical mechanics. Thermodynamical quantiti...
Within a Kuhn-Tucker cavity method introduced in a former paper, we study optimal stability learning...
Various applications of the mean field theory (MFT) technique for obtaining solutions close to optim...
An input-output map in which the patterns are divided into classes is considered for the perceptron....
Learning Markov random field (MRF) models is notoriously hard due to the presence of a global norm...
Zero temperature Gibbs learning is considered for a connected committee machine with K hidden units....
We study two different unsupervised learning strategies for a single-layer perceptron. The environme...
The cerebellum is a brain structure which has been traditionally devoted to supervised learning. Acc...
International audienceIn this paper we consider a measure-theoretical formulation of the training of...
Abstract We consider the generalization problem for a perceptron with binary synapses, implementing ...