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Overview DevOps demand is rising driven by cloud adoption, automation, and continuous delivery pipelines across startups, ...

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Training today’s largest neural networks is limited by backpropagation’s sequential updates and Graphics Processing Unit (GPU) bound compute and energy, slowing scaling. We demonstrate a hybrid ...

Sepsis, a critical illness with extremely high mortality, has its patient prognosis early predicted as crucial to improving therapeutic outcomes. Early and accurate risk stratification is particularly ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Abstract: In this article, we design a deep learning based pedestrian dead reckoning (DL-PDR) framework with vision anchor augmentation for seamless centimeter-level indoor positioning. The DL-PDR ...

Drone images have allowed researchers to explore new questions due to their incredible spatial resolution. Ironically however, the level of detail renders traditional image analysis methods largely ...

ABSTRACT: This paper explores the application of various time series prediction models to forecast graphical processing unit (GPU) utilization and power draw for machine learning applications using ...

Deep learning, a branch of artificial intelligence (AI), imitates human thinking and learning by enabling computers to learn from data examples. At its foundation are ‘artificial neural networks,’ ...