Add to ORKGIn recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Computer Science and many other fields. NNs can be used as universal approximators, that is, a tool for regressing a dependent variable on a possibly complicated function of the explanatory variables. The NN parameters, unfortunately, are notoriously hard to interpret. Under the Bayesian view, we propose and discuss prior distributions for some of the network parameters which encourage parsimony and reduce overfit, by eliminating redundancy, promoting orthogonality, linearity or additivity. Thus we consider more senses of parsimony than are discussed in the existing literature. We investigate the predictive performance of networks fit under ...
The need for function estimation in label-limited settings is common in the natural sciences. At the...
| openaire: EC/H2020/101016775/EU//INTERVENEEncoding domain knowledge into the prior over the high-d...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
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, ...
Neural Networks are famous for their advantageous flexibility for problems when there is insufficie...
While many implementations of Bayesian neural networks use large, complex hierarchical priors, in mu...
Bayesian neural networks attempt to combine the strong predictive performance of neural networks wit...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
The Bayesian treatment of neural networks dictates that a prior distribution is specified over their...
Bayesian neural networks have shown great promise in many applications where calibrated uncertainty ...
The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to bu...
Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Rea...
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 different fields, but have onl...
The need for function estimation in label-limited settings is common in the natural sciences. At the...
| openaire: EC/H2020/101016775/EU//INTERVENEEncoding domain knowledge into the prior over the high-d...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
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, ...
Neural Networks are famous for their advantageous flexibility for problems when there is insufficie...
While many implementations of Bayesian neural networks use large, complex hierarchical priors, in mu...
Bayesian neural networks attempt to combine the strong predictive performance of neural networks wit...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
The Bayesian treatment of neural networks dictates that a prior distribution is specified over their...
Bayesian neural networks have shown great promise in many applications where calibrated uncertainty ...
The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to bu...
Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Rea...
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 different fields, but have onl...
The need for function estimation in label-limited settings is common in the natural sciences. At the...
| openaire: EC/H2020/101016775/EU//INTERVENEEncoding domain knowledge into the prior over the high-d...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...