受强制性开放获取政策约束的文章 - J. Austin Ellis了解详情
可在其他位置公开访问的文章：18 篇
Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks
JA Ellis, L Fiedler, GA Popoola, NA Modine, JA Stephens, AP Thompson, ...
Physical Review B 104 (3), 035120, 2021
强制性开放获取政策： US Department of Energy, Federal Ministry of Education and Research, Germany
Local improvement results for Anderson acceleration with inaccurate function evaluations
A Toth, JA Ellis, T Evans, S Hamilton, CT Kelley, R Pawlowski, S Slattery
SIAM Journal on Scientific Computing 39 (5), S47-S65, 2017
强制性开放获取政策： US National Science Foundation, US Department of Energy
Revealing power, energy and thermal dynamics of a 200pf pre-exascale supercomputer
W Shin, V Oles, AM Karimi, JA Ellis, F Wang
Proceedings of the International Conference for High Performance Computing …, 2021
强制性开放获取政策： US Department of Energy
Scalable inference for sparse deep neural networks using Kokkos kernels
JA Ellis, S Rajamanickam
2019 IEEE High Performance Extreme Computing Conference (HPEC), 1-7, 2019
强制性开放获取政策： US Department of Energy
Co-design center for exascale machine learning technologies (exalearn)
FJ Alexander, J Ang, JA Bilbrey, J Balewski, T Casey, R Chard, J Choi, ...
The International Journal of High Performance Computing Applications 35 (6 …, 2021
强制性开放获取政策： US Department of Energy
Optimization of processor allocation for domain decomposed Monte Carlo calculations
JA Ellis, TM Evans, SP Hamilton, CT Kelley, TM Pandya
Parallel Computing 87, 77-86, 2019
强制性开放获取政策： US National Science Foundation, US Department of Energy
ALO-NMF: Accelerated locality-optimized non-negative matrix factorization
GE Moon, JA Ellis, A Sukumaran-Rajam, S Parthasarathy, P Sadayappan
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
强制性开放获取政策： US National Science Foundation, US Department of Energy
Climbing the summit and pushing the frontier of mixed precision benchmarks at extreme scale
H Lu, M Matheson, V Oles, A Ellis, W Joubert, F Wang
SC22: International Conference for High Performance Computing, Networking …, 2022
强制性开放获取政策： US Department of Energy
Training-free hyperparameter optimization of neural networks for electronic structures in matter
L Fiedler, N Hoffmann, P Mohammed, GA Popoola, T Yovell, V Oles, ...
Machine Learning: Science and Technology 3 (4), 045008, 2022
强制性开放获取政策： US Department of Energy, Federal Ministry of Education and Research, Germany
ECP report: Update on proxy applications and vendor interactions
J Ang, C Sweeney, M Wolf, JA Ellis, S Ghosh, A Kagawa, Y Huang, ...
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020
强制性开放获取政策： US Department of Energy
Hypothesis-agnostic network-based analysis of real-world data suggests ondansetron is associated with lower COVID-19 any cause mortality
GM Miller, JA Ellis, R Sarangarajan, A Parikh, LO Rodrigues, C Bruce, ...
Drugs-real world outcomes 9 (3), 359-375, 2022
强制性开放获取政策： US Department of Energy
Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis
GM Miller, JA Ellis, R Sarangarajan, A Parikh, LO Rodrigues, C Bruce, ...
medRxiv, 2021.10. 05.21264578, 2021
强制性开放获取政策： US Department of Energy
A machine learning surrogate for density functional theory based on the local density of state.
N Modine, L Fiedler, D Vogel, A Thompson, A Ellis, J Stephens, ...
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022
强制性开放获取政策： US Department of Energy
Finding Electronic Structure Machine Learning Surrogates without Training
L Fiedler, N Hoffmann, P Mohammed, GA Popoola, T Yovell, V Oles, ...
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022
强制性开放获取政策： US Department of Energy, Federal Ministry of Education and Research, Germany
Accelerating Multiscale Materials Modeling with Machine Learning.
J Ellis
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020
强制性开放获取政策： US Department of Energy
miniGAN: A Generative Adversarial Network proxy application WBS 2.2. 6.08 ECP-2.1. 3 (Q3 FY2020 Milestone Report)(V. 1.0)
JA Ellis
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020
强制性开放获取政策： US Department of Energy
Scalable inference for sparse neural networks using Kokkos Kernels.
J Ellis, S Rajamanickam
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2019
强制性开放获取政策： US Department of Energy
Understanding the Machine Learning Needs of ECP Applications
JA Ellis, S Rajamanickam
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2019
强制性开放获取政策： US Department of Energy
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