11 citations · 28 across the 5 of their papers we have counts for
5 papers
Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation
Ryan Benkert, Oluwaseun Joseph Aribido, Ghassan AlRegib
In recent years, deep neural networks have significantly impacted the seismic interpretation process. Due to the simple implementation and low interpretation costs, deep neural net…
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…