11 citations · 28 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…