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Deep Active Learning for Dialogue Generation Nadia Affair, Pascal Part, In Jiang, Hang Li Cheri ton School of Computer Science, University of Waterloo, Canada NASCAR, popular Waterloo.ca Noah's Ark
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Deep active learning is a machine learning technique that aims to reduce the amount of labeled data needed for training by allowing the model to select which data points to learn from.
Deep active learning is typically used by data scientists and machine learning engineers working on projects where labeled data is scarce.
To fill out deep active learning, one must first choose a model architecture and then implement a query strategy to select which data points to label next.
The purpose of deep active learning is to improve the efficiency of the training process by reducing the amount of labeling required for training a machine learning model.
The information reported on deep active learning includes the data points selected for labeling, the model's predictions, and the true labels of the data points.
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