10 citations · 18 across the 8 of their papers we have counts for
8 papers
Targeting Negative Flips in Active Learning using Validation Sets
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second wa…
Effective Data Selection for Seismic Interpretation through Disagreement
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
This paper presents a discussion on data selection for deep learning in the field of seismic interpretation. In order to achieve a robust generalization to the target volume, it is…
Transitional Uncertainty with Layered Intermediate Predictions
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
In this paper, we discuss feature engineering for single-pass uncertainty estimation. For accurate uncertainty estimates, neural networks must extract differences in the feature sp…
FOCAL: A Cost-Aware Video Dataset for Active Learning
Kiran Kokilepersaud, Yash-Yee Logan, Ryan Benkert +6
In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning…
Gaussian Switch Sampling: A Second Order Approach to Active Learning
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib +2
In active learning, acquisition functions define informativeness directly on the representation position within the model manifold. However, for most machine learning models (in pa…
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
This paper considers deep out-of-distribution active learning. In practice, fully trained neural networks interact randomly with out-of-distribution (OOD) inputs and map aberrant s…
Explaining Deep Models through Forgettable Learning Dynamics
Ryan Benkert, Oluwaseun Joseph Aribido, Ghassan AlRegib
Even though deep neural networks have shown tremendous success in countless applications, explaining model behaviour or predictions is an open research problem. In this paper, we a…
Patient Aware Active Learning for Fine-Grained OCT Classification
Yash-yee Logan, Ryan Benkert, Ahmad Mustafa +2
This paper considers making active learning more sensible from a medical perspective. In practice, a disease manifests itself in different forms across patient cohorts. Existing fr…