Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. Generalisation is measured by the performance on independent test data drawn from the same distribution as the training data. Such performance can be quantified by the posterior average of the information divergence between the true and the model distributions. Averaging over the Bayesian posterior guarantees internal coherence; Using information divergence guarantees invariance with respect to representation. The theory generalises the least mean squares theory for linear Gaussian models to general problems of statistical estimation. The main results are: (1) the ideal optimal estimate is always given by average over the posterior; (2) the o...
Nous argumentons que l'estimation de l'information mutuelle entre des ensembles de variables aléatoi...
Bayesian treatments of learning in neural networks are typically based either on a local Gaussian ap...
Training probability-density estimating neural networks with the expectation-maximization (EM) algor...
Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. ...
Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. ...
Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. ...
Neural networks are statistical models and learning rules are estimators. In this paper a theory for...
Neural networks are statistical models and learning rules are estimators. In this paper a theory for...
The problem of evaluating dierent learning rules and other statistical estimators is analysed. A new...
The problem of evaluating different learning rules and other statistical estimators is analysed. A n...
Neural network learning rules can be viewed as statistical estimators. They should be studied in Bay...
A family of measurements of generalisation is proposed for estimators of continuous distributions. I...
A family of measurements of generalisation is proposed for estimators of continuous distributions. I...
Variational inference with a factorized Gaussian posterior estimate is a widely-used approach for l...
The assessment of the reliability of systems which learn from data is a key issue to investigate tho...
Nous argumentons que l'estimation de l'information mutuelle entre des ensembles de variables aléatoi...
Bayesian treatments of learning in neural networks are typically based either on a local Gaussian ap...
Training probability-density estimating neural networks with the expectation-maximization (EM) algor...
Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. ...
Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. ...
Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. ...
Neural networks are statistical models and learning rules are estimators. In this paper a theory for...
Neural networks are statistical models and learning rules are estimators. In this paper a theory for...
The problem of evaluating dierent learning rules and other statistical estimators is analysed. A new...
The problem of evaluating different learning rules and other statistical estimators is analysed. A n...
Neural network learning rules can be viewed as statistical estimators. They should be studied in Bay...
A family of measurements of generalisation is proposed for estimators of continuous distributions. I...
A family of measurements of generalisation is proposed for estimators of continuous distributions. I...
Variational inference with a factorized Gaussian posterior estimate is a widely-used approach for l...
The assessment of the reliability of systems which learn from data is a key issue to investigate tho...
Nous argumentons que l'estimation de l'information mutuelle entre des ensembles de variables aléatoi...
Bayesian treatments of learning in neural networks are typically based either on a local Gaussian ap...
Training probability-density estimating neural networks with the expectation-maximization (EM) algor...
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