Add to ORKGNeural Networks are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand this flexibility can cause overfitting and can hamper the generalization properties of neural networks. Many approaches to regularize NN have been suggested but most of them based on ad-hoc arguments. Employing the principle of transformation invariance we derive a general prior in accordance with the Bayesian probability theory for a class of feedforward networks. Optimal networks are determined by Bayesian model comparison verifying the applicability of this approach
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
Bayesian neural networks attempt to combine the strong predictive performance of neural networks wit...
Approximate marginal Bayesian computation and inference are developed for neural network models. The...
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
Deep neural networks have bested notable benchmarks across computer vision, reinforcement learning, ...
Recent studies have shown that the generalization ability of deep neural networks (DNNs) is closely ...
While many implementations of Bayesian neural networks use large, complex hierarchical priors, in mu...
Existing Bayesian treatments of neural networks are typically characterized by weak prior and approx...
24 pages, including 2 pages of references and 10 pages of appendixIn machine learning, it is common ...
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes....
The Bayesian treatment of neural networks dictates that a prior distribution is specified over their...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
The paper deals with learning probability distributions of observed data by artificial neural networ...
International audienceWe investigate deep Bayesian neural networks with Gaussian priors on the weigh...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
Bayesian neural networks attempt to combine the strong predictive performance of neural networks wit...
Approximate marginal Bayesian computation and inference are developed for neural network models. The...
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
Deep neural networks have bested notable benchmarks across computer vision, reinforcement learning, ...
Recent studies have shown that the generalization ability of deep neural networks (DNNs) is closely ...
While many implementations of Bayesian neural networks use large, complex hierarchical priors, in mu...
Existing Bayesian treatments of neural networks are typically characterized by weak prior and approx...
24 pages, including 2 pages of references and 10 pages of appendixIn machine learning, it is common ...
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes....
The Bayesian treatment of neural networks dictates that a prior distribution is specified over their...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
The paper deals with learning probability distributions of observed data by artificial neural networ...
International audienceWe investigate deep Bayesian neural networks with Gaussian priors on the weigh...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
Bayesian neural networks attempt to combine the strong predictive performance of neural networks wit...
Approximate marginal Bayesian computation and inference are developed for neural network models. The...