17 citations · 30 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 17 cited
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification
Garrett Beatty, Ethan Kochis, Michael Bloodgood
Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training da…
cs.LG2018★ 13 cited
Impact of Batch Size on Stopping Active Learning for Text Classification
Garrett Beatty, Ethan Kochis, Michael Bloodgood
When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective. However, in practice due to speed and human annotator considera…