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
Q1 SJR score
351
Citations
11
Influential Citations
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.
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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References
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Citations
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Bayesian meta learning for trustworthy uncertainty quantification
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2024·0citations·Zhenyuan Yuan et al.·ArXiv
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Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation
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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.
2024·2citations·Michelle Zhao et al.·ArXiv
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