The rapid growth and adaptation of medical information to identify significant health trends
and help with timely preventive care have been recent hallmarks of the modern healthcare …

The feasibility of Federated Learning (FL) is highly dependent on the training and inference
capabilities of local models, which are subject to the availability of meaningful and …

It is often the case that collections of documents are annotated with hierarchically-structured
concepts. However, the benefits of this structure are rarely taken into account by …

While collections of documents are often annotated with hierarchically structured concepts,
the benefits of these structures are rarely taken into account by classification techniques …

Multiclass multilabel classification is the task of attributing multiple labels to examples via
predictions. Current models formulate a reduction of the multilabel setting into either multiple …

The peer merit review of research proposals has been the major mechanism to decide grant
awards. However, research proposals have become increasingly interdisciplinary. It has …

Abstract Multi-label Active Learning (MLAL) is an effective method to improve the
performance of the classifier on multi-label problems with less annotation effort by allowing …

Graph neural networks (GNNs) have recently achieved impressive progress on graph-based
semi-supervised learning. The active learning of GNNs aims to select a small number of …

To advance the development of science and technology, research proposals are submitted
to open-court competitive programs developed by government agencies (eg, NSF). Proposal …

In medical filed, predicting the occurrence of heart diseases is a significant piece of work.
Millions of healthcare‐related complexities that have remained unsolved up until now can …