Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Subil Abraham"'
Autor:
Subil Abraham, Paul Abston, Ryan Adamson, Valentine Anantharaj, Aaron Barlow, Ashley Barker, Tom Beck, Katie Bethea, Adam Carlyle, Dan Dietz, Christopher Fuson, Jason Hill, Ryan Landfield, Don Maxwell, Rachel McDowell, Bronson Miller, Sheila Moore, Sarp Oral, Thomas Papatheodore, Suzanne Parete-Koon, Ryan Prout, Sherry Ray, William Renaud, Jim Rogers II, Mallikarjun (Arjun) Shankar, Woong Shin, Scott Simmerman, Betsy Sonewald, Kevin Thach, Aristeidis (aris) Tsaris, Georgia Tourassi, Justin Whitt, Junqi Yin, Benjamin Hernandez Arreguin, Veronica Melesse Vergara
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f365591b1aa56f5b260a7cb8af86149
https://doi.org/10.2172/1968683
https://doi.org/10.2172/1968683
High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function
Autor:
Mu Gao, Peik Lund-Andersen, Alex Morehead, Sajid Mahmud, Chen Chen, Xiao Chen, Nabin Giri, Raj S. Roy, Farhan Quadir, T. Chad Effler, Ryan Prout, Subil Abraham, Wael Elwasif, N. Quentin Haas, Jeffrey Skolnick, Jianlin Cheng, Ada Sedova
Publikováno v:
Workshop Mach Learn HPC Environ
Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcf4dad9e4fcf57445a21c0660acc9c7
https://pubmed.ncbi.nlm.nih.gov/35112110
https://pubmed.ncbi.nlm.nih.gov/35112110
Publikováno v:
CLOUD
The lightweight nature, application portability, and deployment flexibility of containers is driving their widespread adoption in cloud solutions. Data analysis and deep learning (DL)/machine learning (ML) applications have especially benefited from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eea9753d151037819f91d1e52b2ee0e9
https://doi.org/10.1109/cloud49709.2020.00048
https://doi.org/10.1109/cloud49709.2020.00048