This is an implementation of our paper: "An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets", which is updated from ...

Before diving into the details, let’s look at a high-level overview outlining vocabulary terms we’ll see come up and contrasting different methods. It would also be useful to revisit this section ...

Objectives: to analyze the scientific evidence on the strategies of active learning methodologies used in the training of nurses, as well as their contributions and obstacles in training. Methods: ...

Small-Text provides state-of-the-art Active Learning for Text Classification. Several pre-implemented Query Strategies, Initialization Strategies, and Stopping Criteria are provided, which can be ...

Abstract: Active learning (AL) is a machine-learning approach for reducing the data labeling effort. Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a model ...

When the ground near Leilani Estates opened and began spilling out noxious fumes and lava from Mount Kilauea last week, residents of the neighborhood near the Hawaiian volcano fled. Several hundred ...

Abstract: Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled ...