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Learning how to Active Learn: A Deep Reinforcement Learning Approach
Active learning aims to select a small subset of data for annotation suc...
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Deep Active Learning via Open Set Recognition
In many applications, data is easy to acquire but expensive and time con...
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Task-Aware Variational Adversarial Active Learning
Deep learning has achieved remarkable performance in various tasks thank...
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Active Learning for Speech Recognition: the Power of Gradients
In training speech recognition systems, labeling audio clips can be expe...
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Active Learning in the Overparameterized and Interpolating Regime
Overparameterized models that interpolate training data often display su...
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The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning
State-of-the-art deep neural network recognition systems are designed fo...
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Probabilistic Active Learning of Functions in Structural Causal Models
We consider the problem of learning the functions computing children fro...
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Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking). We introduce an active learning method that leverages underlying structure of a continually refined, learned latent space to select the most informative examples to label. This enables the selection of the most informative examples that progressively increases the coverage on the universe of symptoms via the learned model, despite the long tail in data distribution.
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