Add to ORKGthis article may suggest that the research reported herein may be more fundamental than it really is! The foundations of Bayesian techniques in neural networks have been already laid down in the works of Buntine and Weigend (1991), MacKay (1996), Neal (1996), and Wolpert (1993). Bishop (1996) devotes an entire chapter to the application of Bayesian techniques in neural networks. The present article aims to only illustrate some of the ideas developed in these work
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
Studying interactions between different brain regions or neural components is crucial in understandi...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
This article reviews current advances and developments in neural networks. This requires recalling s...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
The true posterior distribution of a Bayesian neural network is massively multimodal. Whilst most of...
Several recent contributions have suggested to consider neural networks, obtained through supervized...
According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal i...
Embodied agents, be they animals or robots, acquire information about the world through their senses...
According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal i...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
Studying interactions between different brain regions or neural components is crucial in understandi...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
This article reviews current advances and developments in neural networks. This requires recalling s...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
The true posterior distribution of a Bayesian neural network is massively multimodal. Whilst most of...
Several recent contributions have suggested to consider neural networks, obtained through supervized...
According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal i...
Embodied agents, be they animals or robots, acquire information about the world through their senses...
According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal i...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
Studying interactions between different brain regions or neural components is crucial in understandi...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...