Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Techniques from sparse signal representation are beginning to see significant impact in
computer vision, often on nontraditional applications where the goal is not just to obtain a …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …

It is often difficult for a machine learning model trained based on a small size of
charge/discharge cycling data to produce satisfactory accuracy in the capacity estimation of …

Automatic detection of diseases by use of computers is an important, but still unexplored
field of research. Such innovations may improve medical practice and refine health care …

Multi-task learning in Convolutional Networks has displayed remarkable success in the field
of recognition. This success can be largely attributed to learning shared representations …

For the data-driven remaining useful life (RUL) prediction for rolling bearings, the traditional
machine learning-based methods generally provide insufficient feature representation and …

Malaria is an infectious disease caused by single-celled parasite of plasmodium group. The
disease is more often spread by an Infected Female Anopheles mosquito. In 2017 alone 219 …

Highway work zones are most vulnerable roadway segments for congestion and traffic
collisions. Hence, providing accurate and timely prediction of the severity of traffic collisions …