Spatially informed Bayesian neural network for neurodegenerative diseases classification
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9604
Abstract: Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis of neurodegenerative diseases. One field of extensive clinical use of MRI is the accurate and automated classification of degenerative disorders. Most… read more here.
Keywords: neural network; bayesian neural; informed bayesian; spatially informed ... See more keywords
A Bayesian neural network methodology to predict the liquid phase diffusion coefficient
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Heat and Mass Transfer"
DOI: 10.1016/j.ijheatmasstransfer.2020.120309
Abstract: Abstract This paper deals with the development of an Artificial Neural Network methodology for the prediction of the liquid phase diffusion coefficient between species at infinite dilution in binary mixtures. The proposed methodology was implemented… read more here.
Keywords: bayesian neural; neural network; methodology; diffusion coefficient ... See more keywords
Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.04.103
Abstract: Abstract Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks. However, recent works have pointed out that the outputs provided by these models are not well-calibrated, seriously limiting their use in critical… read more here.
Keywords: calibration; neural networks; bayesian neural; calibration deep ... See more keywords
Probabilistic inference of Bayesian neural networks with generalized expectation propagation
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.060
Abstract: Abstract Deep learning plays an important role in the field of machine learning. However, deterministic methods such as neural networks cannot capture the model uncertainty. Bayesian neural network (BNN) are recently under consideration since Bayesian… read more here.
Keywords: neural networks; approximate inference; method; expectation propagation ... See more keywords
Explainable Artificial Intelligence for Bayesian Neural Networks: Toward Trustworthy Predictions of Ocean Dynamics
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Advances in Modeling Earth Systems"
DOI: 10.1029/2022ms003162
Abstract: The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision‐making… read more here.
Keywords: explainable artificial; neural network; bnn; neural networks ... See more keywords
A Bayesian neural network predicts the dissolution of compact planetary systems
Sign Up to like & getrecommendations! Published in 2021 at "Proceedings of the National Academy of Sciences of the United States of America"
DOI: 10.1073/pnas.2026053118
Abstract: Significance Despite over 300 y of effort, no solutions exist for predicting when a general planetary configuration will become unstable. We introduce a deep learning architecture to push forward this problem for compact systems. While… read more here.
Keywords: compact planetary; bayesian neural; model; neural network ... See more keywords
Bayesian Neural Networks for Selection of Drug Sensitive Genes
Sign Up to like & getrecommendations! Published in 2018 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2017.1409122
Abstract: ABSTRACT Recent advances in high-throughput biotechnologies have provided an unprecedented opportunity for biomarker discovery, which, from a statistical point of view, can be cast as a variable selection problem. This problem is challenging due to… read more here.
Keywords: neural networks; networks selection; selection; drug ... See more keywords
Physics-Prior Bayesian Neural Networks in Semiconductor Processing
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2940130
Abstract: With the fast scaling-down and evolution of integrated circuit (IC) manufacturing technology, the fabrication process becomes highly complex, and the experimental cost of the processes is significantly elevated. Therefore, in many cases, it is very… read more here.
Keywords: prior bayesian; physics prior; semiconductor; physics ... See more keywords
Bayesian Neural Networks for Reversible Steganography
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3159911
Abstract: Recent advances in deep learning have led to a paradigm shift in the field of reversible steganography. A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks. However,… read more here.
Keywords: neural networks; bayesian neural; uncertainty; reversible steganography ... See more keywords
Bayesian Neural Networks to Analyze Hyperspectral Datasets Using Uncertainty Metrics
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3205119
Abstract: Machine learning techniques, and specifically neural networks, have proved to be very useful tools for image classification tasks. Nevertheless, measuring the reliability of these networks and calibrating them accurately are very complex. This is even… read more here.
Keywords: neural networks; bayesian neural; networks analyze; uncertainty metrics ... See more keywords
An Energy-Efficient Bayesian Neural Network Implementation Using Stochastic Computing Method.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3265533
Abstract: The robustness of Bayesian neural networks (BNNs) to real-world uncertainties and incompleteness has led to their application in some safety-critical fields. However, evaluating uncertainty during BNN inference requires repeated sampling and feed-forward computing, making them… read more here.
Keywords: bayesian neural; efficient bayesian; energy; stochastic computing ... See more keywords