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Into the ML-Universe: An improved classification and characterization of machine-learning projects

Journal of Systems and Software
Vincenzo De Martino, Gilberto Recupito, Giammaria Giordano, Filomena Ferrucci, Dario Di Nucci, Fabio Palomba   +5 more
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Machine Learning with Snap!-ML-Sprites

, 2020
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Machine learning (ML)-based lithography optimizations

2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), 2016
Recent lithography optimizations demand higher accuracy and cause longer runtime. Optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion, for example, take a few days due to lengthy lithography simulations and high pattern density.
Seongbo Shim, Suhyeong Choi, Youngsoo Shin   +2 more
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ML for RT: Priority Assignment Using Machine Learning

2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS), 2021
As machine learning (ML) has been proven effective in solving various problems, researchers in the real-time systems (RT) community have recently paid increasing attention to ML. While most of them focused on timing issues for ML applications (i.e., RT for ML), only a little has been done on the use of ML for solving fundamental RT problems.
Seunghoon Lee 0002, Hyeongboo Baek, Honguk Woo, Kang G. Shin, Jinkyu Lee 0001   +4 more
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Machine Learning with Core ML

2020
This chapter introduces the Core ML API and shows how it is now possible to use contemporary machine learning models to implement intelligent image analysis and computer vision solutions, such as object detection and recognition and scene classification.
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Azure Machine Learning (ML) Workbench

2019
Azure ML Workbench is another tool introduced by Microsoft in 2017. Azure Machine Learning services (preview) integrate end-to-end data science with advanced analytics tools. They help professional data scientists prepare data, develop experiments, and deploy models at cloud scale [1]. First in this chapter, a brief introduction into Azure ML Workbench
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