Bayesian approach for neural networks--review and case studies

doi:10.1016/S0893-6080(00)00098-8

Paper

Bayesian approach for neural networks--review and case studies

Published Apr 1, 2001 · J. Lampinen, Aki Vehtari

Neural networks : the official journal of the International Neural Network Society

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351

Citations

11

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Abstract

Abstract removed due to Elsevier request; this does not indicate any issues with the research. Click the full text link above to read the abstract and view the original source.

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Study Snapshot

Key takeawayThe Bayesian approach for neural network learning allows for more generalization and less restrictive priors, making it more effective in regression, classification, and inverse problems.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Bayesian approach for neural networks--review and case studies

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References

Citations

Optimizing MCMC-Driven Bayesian Neural Networks for High-Precision Medical Image Classification in Small Sample Sizes

MCMC-based Bayesian neural networks improve robustness and accuracy in medical image classification with small samples, reaching 85% for lung X-ray and 88% for breast tissue slice datasets.

Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data

The Taylor-Sensus Network (TSNet) effectively models complex, heteroscedastic noise in scientific data, improving uncertainty estimation and demonstrating superior performance in experiments.

Bayesian Neural Network Variational Autoencoder Inverse Mapper (BNN-VAIM) and its application in Compton Form Factors extraction

The Bayesian Neural Network Variational Autoencoder Inverse Mapper (BNN-VAIM) effectively extracts Compton Form Factors from unpolarized Deeply virtual exclusive scattering (DVCS) cross sections, reducing uncertainty in the inverse problem.

Fatigue Load Modeling of Floating Wind Turbines Based on Vine Copula Theory and Machine Learning

The RF machine learning model accurately predicts flapwise fatigue loads at the blade root of floating wind turbines with up to 99.97% accuracy, improving their reliability and efficiency.

Bayesian meta learning for trustworthy uncertainty quantification

Trust-Bayes is a novel Bayesian meta learning framework that optimizes for trustworthy uncertainty quantification in Bayesian regression, reducing sample complexity and improving accuracy.

posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms

posteriordb is a database of 120 representative models and data sets for evaluating and comparing Bayesian inference algorithms across various target densities.

Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation

The rPINN method provides faster and more accurate uncertainty quantification in inverse PDE problems compared to the HMC method, making it a promising alternative for non-linear problems.

Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions

Conformalized assistive teleoperation effectively maps low-dimensional human inputs to high-dimensional robot actions, improving robot performance in various tasks.