| Fashionable modelling with flux M Innes, E Saba, K Fischer, D Gandhi, MC Rudilosso, NM Joy, T Karmali, ... arXiv preprint arXiv:1811.01457, 2018 | 247 | 2018 |
| A differentiable programming system to bridge machine learning and scientific computing M Innes, A Edelman, K Fischer, C Rackauckas, E Saba, VB Shah, ... arXiv preprint arXiv:1907.07587, 2019 | 234 | 2019 |
| Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs L Yang, S Treichler, T Kurth, K Fischer, D Barajas-Solano, J Romero, ... 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), 1-11, 2019 | 56 | 2019 |
| Cataloging the visible universe through Bayesian inference in Julia at petascale J Regier, K Fischer, K Pamnany, A Noack, J Revels, M Lam, S Howard, ... Journal of Parallel and Distributed Computing 127, 89-104, 2019 | 42 | 2019 |
| Generalized physics-informed learning through language-wide differentiable programming C Rackauckas, A Edelman, K Fischer, M Innes, E Saba, VB Shah, ... | 40 | 2021 |
| A differentiable programming system to bridge machine learning and scientific computing.(2019) M Innes, A Edelman, K Fischer, C Rackauckas, E Saba, VB Shah, ... arXiv preprint arXiv:1907.07587, 1907 | 19 | 1907 |
| Automatic full compilation of Julia programs and ML models to cloud TPUs K Fischer, E Saba arXiv preprint arXiv:1810.09868, 2018 | 16 | 2018 |
| Fashionable modelling with flux. CoRR abs/1811.01457 (2018) M Innes, E Saba, K Fischer, D Gandhi, MC Rudilosso, NM Joy, T Karmali, ... arXiv preprint arXiv:1811.01457, 2018 | 16 | 2018 |
| Fashionable modelling with flux, 2018 M Innes, E Saba, K Fischer, D Gandhi, MC Rudilosso, NM Joy, T Karmali, ... arXiv preprint arXiv:1811.01457, 1811 | 15 | 1811 |
| A differentiable programming system to bridge machine learning and scientific computing, arXiv M Innes, A Edelman, K Fischer, C Rackauckas, E Saba, VB Shah, ... arXiv preprint arXiv:1907.07587, 2019 | 12 | 2019 |
| Fashionable Modelling with Flux, CoRR, abs/1811.01457 M Innes, E Saba, K Fischer, D Gandhi, MC Rudilosso, NM Joy, T Karmali, ... arXiv preprint arXiv:1811.01457, 2018 | 10 | 2018 |
| Composable and reusable neural surrogates to predict system response of causal model components R Anantharaman, A Abdelrehim, F Martinuzzi, S Yalburgi, E Saba, ... AAAI 2022 Workshop on AI for Design and Manufacturing (ADAM), 2021 | 7 | 2021 |
| CoRR abs/1811.01457 (2018) M Innes, E Saba, K Fischer, D Gandhi, MC Rudilosso, NM Joy, T Karmali, ... arXiv preprint arXiv:1811.01457, 0 | 5 | |
| A differentiable programming system to bridge machine learning and scientific computing. arXiv 2019 M Innes, A Edelman, K Fischer, C Rackauckas, E Saba, VB Shah, ... arXiv preprint arXiv:1907.07587, 0 | 5 | |
| Blind adaptive beamforming of narrowband signals using an uncalibrated antenna-array by machine learning S Schoenbrod, E Saba, M Bazdresch, S Kelly, T Besard, K Fischer 2022 IEEE International Symposium on Phased Array Systems & Technology (PAST …, 2022 | 3 | 2022 |
| Julia e Flux: modernizando o aprendizado de máquina D Gandhi, M Innes, E Saba, K Fischer, V Shah Computação Brasil, 41-45, 2019 | 3 | 2019 |
| Flux: Julia machine learning library M Innes, E Saba, K Fischer, D Gandhi, M Concetto Rudilosso, ... Astrophysics Source Code Library, ascl: 2110.015, 2021 | 2 | 2021 |
| Compiler systems and methods for accelerated differential algebraic equations (dae) simulation K Fischer, E Saba, MA Yingbo US Patent App. 18/766,120, 2025 | | 2025 |
| Calibrating Reception Characteristics of Antenna Elements and RF-Frontend Within a Phased Array Using Machine Learning Techniques S Schoenbrod, E Saba, M Bazdresch, S Kelly, T Besard, K Fischer 2024 IEEE International Symposium on Phased Array Systems and Technology …, 2024 | | 2024 |
| Compiler transform optimization for non-local functions K Fischer US Patent App. 18/216,969, 2023 | | 2023 |
