Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

While deep learning (DL) is data-hungry and usually relies on extensive labeled data to
deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small …

Investigating active learning, we focus on the relation between the number of labeled
examples (budget size), and suitable querying strategies. Our theoretical analysis shows a …

In this paper, we are proposing a unified and principled method for both the querying and
training processes in deep batch active learning. We are providing theoretical insights from …

Weed identification helps ensure crop yield and realize precision agriculture. Although the
deep learning-based methods have achieved high performance, their needed large-scale …

Deep active learning aims to reduce the annotation cost for the training of deep models,
which is notoriously data-hungry. Until recently, deep active learning methods were …

With the rapid growth of Internet penetration, more and more people choose the Internet to
express their views on topics of interest. In recent years, named entity recognition (NER) is …

Due to their promising performance in clinical risk prediction with Electronic Health Records
(EHRs), deep learning methods have attracted significant interest from healthcare …

We study acquisition functions for active learning (AL) for text classification. The Expected
Loss Reduction (ELR) method focuses on a Bayesian estimate of the reduction in …