Learning to Rank Graph-based Application Objects on Heterogeneous Memories

Author:

Moura Diego1,Petrucci Vinicius1,Mosse Daniel2

Affiliation:

1. University Federal of Bahia, Brazil

2. Pittsburgh University, United States

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

ACM

Reference40 articles.

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2. [n. d.]. Intel-Micron Memory 3D XPoint. Retrieved May 4, 2020 from http://http://intel.ly/1eICR0a [n. d.]. Intel-Micron Memory 3D XPoint. Retrieved May 4, 2020 from http://http://intel.ly/1eICR0a

3. [n. d.]. Intel VTune Profiler. Retrieved May 9, 2021 from https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/vtune-profiler.html#gs.0r7z5o [n. d.]. Intel VTune Profiler. Retrieved May 9, 2021 from https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/vtune-profiler.html#gs.0r7z5o

4. [n. d.]. Loocv for evaluating machine learning algorithms. Retrieved May 9, 2021 from https://machinelearningmastery.com/loocv-for-evaluating-machine-learning-algorithms/ [n. d.]. Loocv for evaluating machine learning algorithms. Retrieved May 9, 2021 from https://machinelearningmastery.com/loocv-for-evaluating-machine-learning-algorithms/

5. [n. d.]. Memcached. Retrieved May 9, 2021 from http://memcached.org [n. d.]. Memcached. Retrieved May 9, 2021 from http://memcached.org

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1. Graph-based comparative analysis of learning to rank datasets;International Journal of Data Science and Analytics;2023-06-30

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