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Querying Easily Flip-flopped Samples for Deep Active Learning
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data.
Active Learning: Sampling in the Least Probable Disagreement Region
This strategy is valid only when the sample's "closeness" to the decision boundary can be estimated.
Least Probable Disagreement Region for Active Learning
Active learning strategy to query unlabeled samples nearer the estimated decision boundary at each step has been known to be effective when the distance from the sample data to the decision boundary can be explicitly evaluated; however, in numerous cases in machine learning, especially when it involves deep learning, conventional distance such as the $\ell_p$ from sample to decision boundary is not readily measurable.
A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit
Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search.
