10 citations · 18 across the 8 of their papers we have counts for
4 papers · 1 filter
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…