6 papers
Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning
Isaac Xu, Martin Gillis, Ayushi Sharma +3
In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for more detailed or fine-grained classific…
Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimation
H. Martin Gillis, Isaac Xu, Thomas Trappenberg
Machine learning applications require fast and reliable per-sample uncertainty estimation. A common approach is to use predictive distributions from Bayesian or approximation metho…
Uncertainty Estimation using Variance-Gated Distributions
H. Martin Gillis, Isaac Xu, Thomas Trappenberg
Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications. A common approach is to use the predicti…
Last-layer committee machines for uncertainty estimations of benthic imagery
H. Martin Gillis, Isaac Xu, Benjamin Misiuk +2
Automating the annotation of benthic imagery (i.e., images of the seafloor and its associated organisms, habitats, and geological features) is critical for monitoring rapidly chang…
Platelet enumeration in dense aggregates
H. Martin Gillis, Yogeshwar Shendye, Paul Hollensen +2
Identifying and counting blood components such as red blood cells, various types of white blood cells, and platelets is a critical task for healthcare practitioners. Deep learning…
Masked strategies for images with small objects
H. Martin Gillis, Ming Hill, Paul Hollensen +2
The hematology analytics used for detection and classification of small blood components is a significant challenge. In particular, when objects exists as small pixel-sized entitie…