… To solve this we have presented an efficient Bayesian convolutional neural network, offering
better robustness to overfitting on small data by placing a probability distribution over the …

… almost all classification tasks, Neural Networks still make over-… is missing from the current
Neural Networks architectures. Very … Bayesian learning to Convolutional Neural Networks that …

… We propose to use the convolutional neural networks in a Bayesian framework to predict
facies based on seismic data and quantify the uncertainty in the classification. A variational …

… In Bayesian CNN (B-CNN) the dropout between weighted layers is incorporated which can
be interpreted as an approximation Bayesian … based Bayesian convolutional neural network …

… Although different approaches have been proposed in the literature, this paper illustrates
a successful implementation of the Bayesian Convolution Neural Networks (BCNN)-based …

… the network partially Bayesian, can … Bayesian NN by being Bayesian in the last B layers
(B ≤ N) layers which makes the first N −B layers behave as a feature extractor for the Bayesian …

… for classification tasks for Bayesian convolutional neural networks with variational inference.
By … The intractable posterior probability distributions over weights are inferred by Bayes by …

… In this article, a Bayesian convolutional neural network (BCNN) is used to quantify the
uncertainties in solving ISPs. With Monte Carlo dropout, the proposed BCNN is able to directly …

Bayesian Convolutional Neural Networks (BCNNs) have emerged as a robust form of Convolutional
Neural Networks … a dropout layer after each convolutional layer in the original CNN. …

… of the convolutional network. From this pre-trained point, we can use Bayesian optimization
to … This work has as contributions: (a) Bayesian estimation of convolutional neural networks …