Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Quang-Khoat Than"'
We demonstrate a machine learning approach designed to extract hidden chemistry/physics to facilitate new materials discovery. In particular, we propose a novel method for learning latent knowledge from material structure data in which machine learni
Externí odkaz:
http://arxiv.org/abs/2101.11902
Publikováno v:
2021 13th International Conference on Knowledge and Systems Engineering (KSE).
We demonstrate a machine learning approach designed to extract hidden chemistry/physics to facilitate new materials discovery. In particular, we propose a novel method for learning latent knowledge from material structure data in which machine learni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::830a9a897dcae5adc7a2dc096b9c31b6
http://arxiv.org/abs/2101.11902
http://arxiv.org/abs/2101.11902
Autor:
Than, Quang Khoat
Supervisor:ホー バオ ツー
知識科学研究科
博士
知識科学研究科
博士
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=jairo_______::7b8d8e6cecfdcfeb2cad2d63db7328f8
http://hdl.handle.net/10119/11555
http://hdl.handle.net/10119/11555
Autor:
Than Quang Khoat
Publikováno v:
RIVF
The best upper time bound for solving the bounded integer programming (BIP) up to now is poly(phi) ldr n2n+o(n), where n and phi are the dimension and the input size of the problem respectively. In this paper, we show that BIP is solvable in determin