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pro vyhledávání: '"Samsonau, Sergey V"'
In this paper, we report the performance benchmarking results of deep learning models on MLCommons' Science cloud-masking benchmark using a high-performance computing cluster at New York University (NYU): NYU Greene. MLCommons is a consortium that de
Externí odkaz:
http://arxiv.org/abs/2403.04553
Autor:
Chennamsetti, Varshitha, von Laszewski, Gregor, Gu, Ruochen, Mehnaz, Laiba, Papay, Juri, Jackson, Samuel, Thiyagalingam, Jeyan, Samsonau, Sergey V., Fox, Geoffrey C.
In this paper, we report on work performed for the MLCommons Science Working Group on the cloud masking benchmark. MLCommons is a consortium that develops and maintains several scientific benchmarks that aim to benefit developments in AI. The benchma
Externí odkaz:
http://arxiv.org/abs/2401.08636
Autor:
Samsonau, Sergey V, Kurbonova, Aziza, Jiang, Lu, Lashen, Hazem, Bai, Jiamu, Merchant, Theresa, Wang, Ruoxi, Mehnaz, Laiba, Wang, Zecheng, Patil, Ishita
We report a framework that enables the wide adoption of authentic research educational methodology at various schools by addressing common barriers. The guiding principles we present were applied to implement a program in which teams of students with
Externí odkaz:
http://arxiv.org/abs/2210.08966
Authentic research as a method of teaching science is gaining popularity in high schools and colleges. To make this research experience most efficient, students need adequate preparation in traditional science courses. Existing materials available fo
Externí odkaz:
http://arxiv.org/abs/1808.02623
Autor:
Samsonau, Sergey V.
Publikováno v:
Physics Education, 53 (2018) 055013
This paper is presenting a set of laboratory classes to be taught as a part of a 1-year calculus-based physics class. It is composed out of 7 modules designed to bring together experiments and computer simulations (numerical simulations). Each module
Externí odkaz:
http://arxiv.org/abs/1804.09264
Autor:
Samsonau, Sergey V, Kurbonova, Aziza, Jiang, Lu, Lashen, Hazem, Bai, Jiamu, Merchant, Theresa, Wang, Ruoxi, Mehnaz, Laiba, Wang, Zecheng, Patil, Ishita
We report a methodology in which students gain experience in authentic research by developing artificial intelligence (AI) solutions for researchers in natural sciences. While creating education benefits for students, our approach also directly benef
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9560880c46b58686c1089c4c97bbcfb3
http://arxiv.org/abs/2210.08966
http://arxiv.org/abs/2210.08966