Uncertainty Estimation using Variance-Gated Distributions

H. Martin Gillis, Isaac Xu, Thomas Trappenberg

Uncertainty quantification for trustworthy deep learning: Methods and measures

H. Martin Gillis, Thomas Trappenberg

The paper surveys methods for quantifying uncertainty in deep neural networks, focusing on ensemble-based and approximate Bayesian approaches and how their outputs are measured.

Covariance Last-Layer Ensembles: Function-Space Diversity for Efficient Uncertainty Quantification

H. Martin Gillis, Isaac Xu, Gabriel Spadon +1

Masked strategies for images with small objects

H. Martin Gillis, Ming Hill, Paul Hollensen +2

Last-layer committee machines for uncertainty estimations of benthic imagery

H. Martin Gillis, Isaac Xu, Benjamin Misiuk +2

Platelet enumeration in dense aggregates

H. Martin Gillis, Yogeshwar Shendye, Paul Hollensen +2

Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimation

H. Martin Gillis, Isaac Xu, Thomas Trappenberg