18 citations · 21 across the 11 of their papers we have counts for
11 papers
Uncertainty quantification for trustworthy deep learning: Methods and measures
H. Martin Gillis, Thomas Trappenberg
The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty qu…
Covariance Last-Layer Ensembles: Function-Space Diversity for Efficient Uncertainty Quantification
H. Martin Gillis, Isaac Xu, Gabriel Spadon +1
A Last-Layer Ensemble (LLE), linear units on one shared frozen feature map, is an efficient single-pass approach to the disagreement-based epistemic uncertainty for out-of-dist…
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…
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…
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…
Hierarchical Multi-Label Classification with Missing Information for Benthic Habitat Imagery
Isaac Xu, Benjamin Misiuk, Scott C. Lowe +3
In this work, we apply state-of-the-art self-supervised learning techniques on a large dataset of seafloor imagery, \textit{BenthicNet}, and study their performance for a complex h…
A Generalized Transformer-based Radio Link Failure Prediction Framework in 5G RANs
Kazi Hasan, Thomas Trappenberg, Israat Haque
Radio link failure (RLF) prediction system in Radio Access Networks (RANs) is critical for ensuring seamless communication and meeting the stringent requirements of high data rates…
BenthicNet: A global compilation of seafloor images for deep learning applications
Scott C. Lowe, Benjamin Misiuk, Isaac Xu +26
Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery…