Bayesian Hierarchical convolutional neural networks

Alexis Bensen; Adam Kahana; Zerotti Woods

doi:10.1117/12.2663968

12 June 2023 Bayesian Hierarchical convolutional neural networks

Alexis Bensen, Adam Kahana, Zerotti Woods

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125380X (2023) https://doi.org/10.1117/12.2663968
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The Hierarchical Bayesian Neural Network (HNN) is a machine learning algorithm that attempts to use the natural hierarchical structure of data. HNN has demonstrated gains in robustness, accuracy, and reporting capabilities by addressing the technical challenge of classifying data at different levels of a hierarchical structure. There is a significant operational benefit in classifying at different levels of an ontology where the extracted knowledge is used for future decision-making, especially when classification at the finest level is unfeasible.

Citation Download Citation

Alexis Bensen, Adam Kahana, and Zerotti Woods "Bayesian Hierarchical convolutional neural networks", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125380X (12 June 2023); https://doi.org/10.1117/12.2663968

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