Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …

In edge computing scenarios, the distribution of data and collaboration of workloads on
different layers are serious concerns for performance, privacy, and security issues. So for …

Today's Internet Services are undergoing fundamental changes and shifting to an intelligent
computing era where AI is widely employed to augment services. In this context, many …

Earlier-stage evaluations of a new AI architecture/system need affordable AI benchmarks.
Only using a few AI component benchmarks like MLPerf alone in the other stages may lead …

Modern businesses benefit significantly from advances in computer vision technology, one
of the important sectors of artificially intelligent and computer science research. Advanced …

In recent years, with the trend of applying deep learning (DL) in high performance scientific
computing, the unique characteristics of emerging DL workloads in HPC raise great …

Federated learning is a new machine learning paradigm. The goal is to build a machine
learning model from the data sets distributed on multiple devices–so-called an isolated data …

Abstract 2D Face recognition has been extensively studied for decades and has reached
remarkable results in recent years. However, 2D Face recognition is sensitive to variations in …

Scientific research communities are embracing AI-based solutions to target tractable
scientific tasks and improve research work flows. However, the development and evaluation …