130 citations · 284 across the 5 of their papers we have counts for
5 papers
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Liu Yang, Sean Treichler, Thorsten Kurth +8
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…
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
Mike Innes, Alan Edelman, Keno Fischer +4
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 model…
Fashionable Modelling with Flux
Michael Innes, Elliot Saba, Keno Fischer +6
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…
Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs
Keno Fischer, Elliot Saba
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 y…
Cataloging the Visible Universe through Bayesian Inference at Petascale
Jeffrey Regier, Kiran Pamnany, Keno Fischer +9
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…