18 citations · 21 across the 8 of their papers we have counts for
8 papers
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…
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…
Label-free Monitoring of Self-Supervised Learning Progress
Isaac Xu, Scott Lowe, Thomas Trappenberg
Self-supervised learning (SSL) is an effective method for exploiting unlabelled data to learn a high-level embedding space that can be used for various downstream tasks. However, e…
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…