From Theories to Queries

199 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control (2021, background): “Active learning is a machine learning method that can reduce training data size while maintaining accuracy by actively exploring regions of the feature space that maximally inform the learning process [82,83].”
Selection Via Proxy: Efficient Data Selection For Deep Learning (2019, background): “Generally speaking, active learning starts with a small amount of labeled data and selects points to label from a much larger pool of unlabeled data [Settles, 2012, Sener and Savarese, 2018, Lewis and Gale, 1994, Lewis and Catlett, 1994, Settles, 2011].”
Human-in-the-loop machine learning: a state of the art (2022, methodology): “By using a mechanism that helps selecting the most relevant examples, the system reduces the amount of data required to train the model, while maximizing (at least maintaining) its accuracy, at a lower cost (Settles 2011).”
Artificial intelligence in recommender systems (2020, background): “Many AI areas related to classiﬁcation or regression problems, such as speech recognition, information retrieval and computational biology, beneﬁt from active learning [47].”