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   Articles with "bayesian neural" as a keyword



Spatially informed Bayesian neural network for neurodegenerative diseases classification

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recommendations! 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

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recommendations! 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

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recommendations! 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

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recommendations! 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

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recommendations! 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

Bayesian neural network with unified entropy source and synapse weights using 3D 16-layer Fe-diode array

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recommendations! Published in 2025 at "Nature Communications"

DOI: 10.1038/s41467-025-63302-8

Abstract: Edge artificial intelligence systems require higher frequency due to intensive computational demands, while most traditional entropy sources decay with frequency. This work shows the physical properties of the Fe-diode devices are ideal for edge systems… read more here.

Keywords: diode array; layer diode; diode; bayesian neural ... See more keywords

A Bayesian neural network predicts the dissolution of compact planetary systems

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recommendations! 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

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recommendations! 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

Bayesian Neural Network-Based Ground Motion Model for Horizontal and V/H Spectral Ordinate with Epistemic Uncertainty for the European Region

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recommendations! Published in 2025 at "Journal of Earthquake Engineering"

DOI: 10.1080/13632469.2025.2528913

Abstract: ABSTRACT Artificial Neural Networks (ANN) have shown wide acceptance in the development of non-parametric Ground motion models (GMMs). Its predictions are, however, point estimates and do not account for epistemic uncertainty. Probabilistic networks like Bayesian… read more here.

Keywords: ground motion; bayesian neural; uncertainty; epistemic uncertainty ... See more keywords

Physics-Prior Bayesian Neural Networks in Semiconductor Processing

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recommendations! 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

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recommendations! 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