Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection

1 code implementation • 21 Mar 2024 • F. M. Castro-Macías, P. Morales-Álvarez, Y. Wu, R. Molina, A. K. Katsaggelos

This approach yields the same variational posterior approximations as the original VGPMIL, which is a consequence of the two representations that the Hyperbolic Secant distribution admits.

Gaussian Processes Multiple Instance Learning +1