Add to ORKGWhile many implementations of Bayesian neural networks use large, complex hierarchical priors, in much of modern Bayesian statistics, noninformative (flat) priors are very common. This paper introduces a noninformative prior for feed-forward neural networks, describing several theoretical and practical advantages of this approach. Details of implementation via Markov chain Monte Carlo are included
Recent studies have shown that the generalization ability of deep neural networks (DNNs) is closely ...
Bayesian inference is known to provide a general framework for incorporating prior knowledge or spec...
Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Rea...
Deep neural networks have bested notable benchmarks across computer vision, reinforcement learning, ...
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
The Bayesian treatment of neural networks dictates that a prior distribution is specified over their...
Neural Networks are famous for their advantageous flexibility for problems when there is insufficie...
The paper deals with learning probability distributions of observed data by artificial neural networ...
Conventional training methods for neural networks involve starting al a random location in the solut...
Bayesian neural networks attempt to combine the strong predictive performance of neural networks wit...
The need for function estimation in label-limited settings is common in the natural sciences. At the...
This paper introduces a new neural network based prior for real valued functions on $\mathbb R^d$ wh...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
Recent studies have shown that the generalization ability of deep neural networks (DNNs) is closely ...
Bayesian inference is known to provide a general framework for incorporating prior knowledge or spec...
Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Rea...
Deep neural networks have bested notable benchmarks across computer vision, reinforcement learning, ...
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
The Bayesian treatment of neural networks dictates that a prior distribution is specified over their...
Neural Networks are famous for their advantageous flexibility for problems when there is insufficie...
The paper deals with learning probability distributions of observed data by artificial neural networ...
Conventional training methods for neural networks involve starting al a random location in the solut...
Bayesian neural networks attempt to combine the strong predictive performance of neural networks wit...
The need for function estimation in label-limited settings is common in the natural sciences. At the...
This paper introduces a new neural network based prior for real valued functions on $\mathbb R^d$ wh...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
Recent studies have shown that the generalization ability of deep neural networks (DNNs) is closely ...
Bayesian inference is known to provide a general framework for incorporating prior knowledge or spec...
Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Rea...