Cataloging the Visible Universe Through Bayesian Inference at Petascale. - Jeffrey Regier; Kiran Pamnany; Keno Fischer; Andreas Noack 001; Maximilian Lam; Jarrett Revels; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (IPDPS, 2018)

Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs. - Keno Fischer; Elliot Saba (CoRR, 2018)

Fashionable Modelling with Flux. - Michael Innes; Elliot Saba; Keno Fischer; Dhairya Gandhi; Marco Concetto Rudilosso; Neethu Mariya Joy; Tejan Karmali; Avik Pal; Viral B. Shah (CoRR, 2018)

Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. - Liu Yang; Prabhat; George E. Karniadakis; Sean Treichler; Thorsten Kurth; Keno Fischer; David A. Barajas-Solano; Joshua Romero; Valentin Churavy; Alexandre M. Tartakovsky; Michael Houston (DLS@SC, 2019)

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing. - Mike Innes; Alan Edelman; Keno Fischer; Christopher Rackauckas; Elliot Saba; Viral B. Shah; Will Tebbutt (CoRR, 2019)

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. - Liu Yang; Sean Treichler; Thorsten Kurth; Keno Fischer; David A. Barajas-Solano; Joshua Romero; Valentin Churavy; Alexandre M. Tartakovsky; Michael Houston; Prabhat; George E. Karniadakis (CoRR, 2019)

Cataloging the visible universe through Bayesian inference in Julia at petascale. - Jeffrey Regier; Keno Fischer; Kiran Pamnany; Andreas Noack 001; Jarrett Revels; Maximilian Lam; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (J. Parallel Distributed Comput., 2019)

Generalized Physics-Informed Learning through Language-Wide Differentiable Programming. - Christopher Rackauckas; Alan Edelman; Keno Fischer; Mike Innes; Elliot Saba; Viral B. Shah; Will Tebbutt (AAAI Spring Symposium: MLPS, 2020)