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pro vyhledávání: '"Fischer, Keno"'
Autor:
Yang, Liu, Treichler, Sean, Kurth, Thorsten, Fischer, Keno, Barajas-Solano, David, Romero, Josh, Churavy, Valentin, Tartakovsky, Alexandre, Houston, Michael, Prabhat, Karniadakis, George
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computati
Externí odkaz:
http://arxiv.org/abs/1910.13444
Autor:
Innes, Mike, Edelman, Alan, Fischer, Keno, Rackauckas, Chris, Saba, Elliot, Shah, Viral B, Tebbutt, Will
Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data. At the same time, machine learning models are becoming increasingly sophisticated and exhibit many features of
Externí odkaz:
http://arxiv.org/abs/1907.07587
Autor:
Innes, Michael, Saba, Elliot, Fischer, Keno, Gandhi, Dhairya, Rudilosso, Marco Concetto, Joy, Neethu Mariya, Karmali, Tejan, Pal, Avik, Shah, Viral
Machine learning as a discipline has seen an incredible surge of interest in recent years due in large part to a perfect storm of new theory, superior tooling, renewed interest in its capabilities. We present in this paper a framework named Flux that
Externí odkaz:
http://arxiv.org/abs/1811.01457
Autor:
Fischer, Keno, Saba, Elliot
Google's Cloud TPUs are a promising new hardware architecture for machine learning workloads. They have powered many of Google's milestone machine learning achievements in recent years. Google has now made TPUs available for general use on their clou
Externí odkaz:
http://arxiv.org/abs/1810.09868
Autor:
Regier, Jeffrey, Pamnany, Kiran, Fischer, Keno, Noack, Andreas, Lam, Maximilian, Revels, Jarrett, Howard, Steve, Giordano, Ryan, Schlegel, David, McAuliffe, Jon, Thomas, Rollin, Prabhat
Astronomical catalogs derived from wide-field imaging surveys are an important tool for understanding the Universe. We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely
Externí odkaz:
http://arxiv.org/abs/1801.10277
Akademický článek
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Publikováno v:
Reviews in Environmental Science & Biotechnology; Dec2021, Vol. 20 Issue 4, p1087-1102, 16p
Akademický článek
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