Hyperbolic secant representation of the logistic function: application to probabilistic multiple instance learning for CT intracranial hemorrhage detection

DOI10.1016/j.artint.2024.104115MaRDI QIDQ6566641

Pablo Morales Álvarez, Yunan N. Wu, Aggelos K. Katsaggelos, Rafael Molina, Francisco M. Castro-Macías

Publication date: 3 July 2024

Published in: Artificial Intelligence (Search for Journal in Brave)

zbMATH Keywords

Gaussian processesvariational inferencemultiple instance learninghyperbolic secant distributionPólya-Gammaintracranial hemorrhage detectionJaakkola bound

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Cites Work

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• Solving the multiple instance problem with axis-parallel rectangles.
• Conditionally conjugate mean-field variational Bayes for logistic models
• 10.1162/15324430152748236
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Generalized Hyperbolic Secant Distributions

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