Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Yury Rodimkov"'
Autor:
Yury Rodimkov, Shikha Bhadoria, Valentin Volokitin, Evgeny Efimenko, Alexey Polovinkin, Thomas Blackburn, Mattias Marklund, Arkady Gonoskov, Iosif Meyerov
Publikováno v:
Sensors, Vol 21, Iss 21, p 6982 (2021)
The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, th
Externí odkaz:
https://doaj.org/article/3c3b37f45d0047e7bd7ebe672cf0bdbb
Autor:
Yury Rodimkov, Evgeny Efimenko, Valentin Volokitin, Elena Panova, Alexey Polovinkin, Iosif Meyerov, Arkady Gonoskov
Publikováno v:
Entropy, Vol 23, Iss 1, p 21 (2020)
When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and no
Externí odkaz:
https://doaj.org/article/cb881d2f44504ad6bb73907b08ddf97b
Autor:
Mattias Marklund, Iosif B. Meyerov, Alexey Polovinkin, Shikha Bhadoria, Evgeny Efimenko, Tom Blackburn, Valentin Volokitin, Arkady Gonoskov, Yury Rodimkov
Publikováno v:
Sensors
Volume 21
Issue 21
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 6982, p 6982 (2021)
Volume 21
Issue 21
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 6982, p 6982 (2021)
The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, th
Autor:
Valentin Volokitin, Iosif B. Meyerov, Yury Rodimkov, Arkady Gonoskov, Evgeny Efimenko, Elena Panova, Alexey Polovinkin
Publikováno v:
Entropy
Volume 23
Issue 1
Entropy, Vol 23, Iss 21, p 21 (2021)
Volume 23
Issue 1
Entropy, Vol 23, Iss 21, p 21 (2021)
When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and no