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pro vyhledávání: '"Naoufal Layad"'
Publikováno v:
Frontiers in Physics, Vol 10 (2022)
We present a case for low batch-size inference with the potential for adaptive training of a lean encoder model. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific use
Externí odkaz:
https://doaj.org/article/d38a53d3cad845f4a80809a07363ae4e
Publikováno v:
Frontiers in Physics, Vol 10 (2022)
The emergence of novel computational hardware is enabling a new paradigm for rapid machine learning model training. For the Department of Energy’s major research facilities, this developing technology will enable a highly adaptive approach to exper
Externí odkaz:
https://doaj.org/article/7024d4a18bd841bdb893a637f1dc3c38
Autor:
Zhengchun Liu, Ahsan Ali, Peter Kenesei, Antonino Miceli, Hemant Sharma, Nicholas Schwarz, Dennis Trujillo, Hyunseung Yoo, Ryan Coffee, Naoufal Layad, Jana Thayer, Ryan Herbst, Chunhong Yoon, Ian Foster
Extremely high data rates at modern synchrotron and X-ray free-electron laser light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes. Regardless of the application, the basic conc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d995c00db1d69e91771c309d4aa65778