Bayesian convolutional neural networks with Bernoulli approximate variational inference
Y Gal, Z Ghahramani - arXiv preprint arXiv:1506.02158, 2015 - arxiv.org
… 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 …
better robustness to overfitting on small data by placing a probability distribution over the …
A comprehensive guide to bayesian convolutional neural network with variational inference
K Shridhar, F Laumann, M Liwicki - arXiv preprint arXiv:1901.02731, 2019 - arxiv.org
… 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 …
Neural Networks architectures. Very … Bayesian learning to Convolutional Neural Networks that …
Bayesian convolutional neural networks for seismic facies classification
… 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 …
facies based on seismic data and quantify the uncertainty in the classification. A variational …
Uncertainty assisted robust tuberculosis identification with bayesian convolutional neural networks
… 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 …
be interpreted as an approximation Bayesian … based Bayesian convolutional neural network …
Bayesian convolutional neural network-based models for diagnosis of blood cancer
ME Billah, F Javed - Applied Artificial Intelligence, 2022 - Taylor & Francis
… Although different approaches have been proposed in the literature, this paper illustrates
a successful implementation of the Bayesian Convolution Neural Networks (BCNN)-based …
a successful implementation of the Bayesian Convolution Neural Networks (BCNN)-based …
FPGA-based acceleration for Bayesian convolutional neural networks
… 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 …
(B ≤ N) layers which makes the first N −B layers behave as a feature extractor for the Bayesian …
Uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference
… for classification tasks for Bayesian convolutional neural networks with variational inference.
By … The intractable posterior probability distributions over weights are inferred by Bayes by …
By … The intractable posterior probability distributions over weights are inferred by Bayes by …
Uncertainty quantification in inverse scattering problems with Bayesian convolutional neural networks
… 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 …
uncertainties in solving ISPs. With Monte Carlo dropout, the proposed BCNN is able to directly …
Fast-BCNN: Massive neuron skipping in Bayesian convolutional neural networks
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. …
Neural Networks … a dropout layer after each convolutional layer in the original CNN. …
Bayesian convolutional neural network estimation for pediatric pneumonia detection and diagnosis
… 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 …
to … This work has as contributions: (a) Bayesian estimation of convolutional neural networks …
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