Over the past few decades, the substantial growth in enterprise-data availability and the
advancements in Artificial Intelligence (AI) have allowed companies to solve real-world …

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …

This article provides the motivation and overview of the Collective Knowledge Framework
(CK or cKnowledge). The CK concept is to decompose research projects into reusable …

Scientific communities are increasingly adopting machine learning and deep learning
models in their applications to accelerate scientific insights. High performance computing …

This work is an introduction and a survey for the Special Issue on Machine Intelligence at the
Edge. The authors argue that workloads that were formerly performed in the cloud are …

The rapid advancement of machine learning (ML) and artificial intelligence (AI) has created
an increasing demand for efficient and automated processes in training and deploying AI …

Deep learning for time series is an emerging area with close ties to industry, yet under
represented in performance benchmarks for machine learning systems. In this paper, we …

Scientific applications are increasingly adopting Artificial Intelligence (AI) techniques to
advance science. High-performance computing centers are evaluating emerging novel …

While GPUs are responsible for training the vast majority of state-of-the-art deep learning
models, the implications of their architecture are often overlooked when designing new deep …

The arrival of 5G together with advances in artificial intelligence, machine learning, cloud
computing, virtualization, and service orchestration have created a ubiquitous computing …