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pro vyhledávání: '"Kan, Maxim"'
Autor:
Kan, Maxim
Publikováno v:
Demographic Research, 2024 Jan 01. 50, 547-582.
Externí odkaz:
https://www.jstor.org/stable/48766153
Autor:
Safin, Aleksandr, Kan, Maxim, Drobyshev, Nikita, Voynov, Oleg, Artemov, Alexey, Filippov, Alexander, Zorin, Denis, Burnaev, Evgeny
Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- an
Externí odkaz:
http://arxiv.org/abs/2105.12038
Autor:
Kan, Maxim, Aliev, Ruslan, Rudenko, Anna, Drobyshev, Nikita, Petrashen, Nikita, Kondrateva, Ekaterina, Sharaev, Maxim, Bernstein, Alexander, Burnaev, Evgeny
Publikováno v:
AIST2020
Deep learning shows high potential for many medical image analysis tasks. Neural networks can work with full-size data without extensive preprocessing and feature generation and, thus, information loss. Recent work has shown that the morphological di
Externí odkaz:
http://arxiv.org/abs/2006.15969
Autor:
Kan, Maxim
This study examines the risks of first, second and third birth in Kazakhstan since the collapse of the Soviet Union through general and ethnic-specific perspectives. Special attention is paid to the economic recovery time after 2000. The most remarka
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-79420
Akademický článek
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Akademický článek
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Autor:
Kan, Maxim
A resurgence of religiosity has been observed in post-Soviet Kazakhstan. However, since the late 1990s, there has been a lack of research on the impact of religion on contraceptive use and the differences in such use across religious groups in the co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1de2613fce122cbaf208961c4355b71d
Autor:
Kan, Maxim, Gall, Julia A., Latypov, Alisher, Gray, Robert, Bakpayev, Marat, Alisheva, Djamila, Rakhmatova, Khursheda, Sadieva, Aigul S.
Publikováno v:
In International Journal of Drug Policy November 2014 25(6):1221-1226
Autor:
Kan, Maxim, Aliev, Ruslan, Rudenko, Anna, Drobyshev, Nikita, Petrashen, Nikita, Kondrateva, Ekaterina, Sharaev, Maxim, Bernstein, Alexander, Burnaev, Evgeny
Publikováno v:
Recent Trends in Analysis of Images, Social Networks and Texts
Deep learning shows high potential for many medical image analysis tasks. Neural networks can work with full-size data without extensive preprocessing and feature generation and, thus, information loss. Recent work has shown that the morphological di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::556d9b2545c54be55e8fd151aa557196
http://europepmc.org/articles/PMC7988406
http://europepmc.org/articles/PMC7988406