Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such
that deep neural networks (DNN) can achieve better performance when trained on this …

Active learning aims to reduce the labeling effort that is required to train algorithms by
learning an acquisition function selecting the most relevant data for which a label should be …

The expensive annotation cost is notoriously known as the main constraint for the
development of the point cloud semantic segmentation technique. Active learning methods …

The determination of people. s unsafe behavior from images in construction has been
typically based on hand-made rule approaches, which renders it difficult to identify multiple …

Label efficiency has become an increasingly important objective in deep learning
applications. Active learning aims to reduce the number of labeled examples needed to train …

The splendid success of convolutional neural networks (CNNs) in computer vision is largely
attributable to the availability of massive annotated datasets, such as ImageNet and Places …

Many real-world ML deployments face the challenge of training a rare category model with a
small labeling bud-get. In these settings, there is often access to large amounts of unlabeled …

With the availability of low-cost and efficient digital cameras, ecologists can now survey the
world's biodiversity through image sensors, especially in the previously rather inaccessible …