no code implementations • 29 Oct 2019 • Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David Barajas-Solano, Josh Romero, Valentin Churavy, Alexandre Tartakovsky, Michael Houston, Prabhat, George Karniadakis
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines.
2 code implementations • 17 Jul 2019 • Mike Innes, Alan Edelman, Keno Fischer, Chris Rackauckas, Elliot Saba, Viral B. Shah, Will Tebbutt
Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data.
2 code implementations • 1 Nov 2018 • Michael Innes, Elliot Saba, Keno Fischer, Dhairya Gandhi, Marco Concetto Rudilosso, Neethu Mariya Joy, Tejan Karmali, Avik Pal, Viral Shah
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.
no code implementations • 23 Oct 2018 • Keno Fischer, Elliot Saba
Google's Cloud TPUs are a promising new hardware architecture for machine learning workloads.
1 code implementation • 31 Jan 2018 • Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin Thomas, Prabhat
We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.
Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics 85A35, 68W10, 62P35 J.2; D.1.3; G.3; I.2; D.2