Mohammad Shoeybi
Mohammad Shoeybi
Director of Applied Research at NVIDIA
Verified email at
Cited by
Cited by
Megatron-lm: Training multi-billion parameter language models using model parallelism
M Shoeybi, M Patwary, R Puri, P LeGresley, J Casper, B Catanzaro
arXiv preprint arXiv:1909.08053, 2019
Bloom: A 176b-parameter open-access multilingual language model
BS Workshop, TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, ...
arXiv preprint arXiv:2211.05100, 2022
Deep voice: Real-time neural text-to-speech
SÖ Arık, M Chrzanowski, A Coates, G Diamos, A Gibiansky, Y Kang, X Li, ...
International conference on machine learning, 195-204, 2017
Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model
S Smith, M Patwary, B Norick, P LeGresley, S Rajbhandari, J Casper, ...
arXiv preprint arXiv:2201.11990, 2022
Efficient large-scale language model training on gpu clusters using megatron-lm
D Narayanan, M Shoeybi, J Casper, P LeGresley, M Patwary, ...
Proceedings of the International Conference for High Performance Computing …, 2021
On the use of the Ffowcs Williams-Hawkings equation to predict far-field jet noise from large-eddy simulations
S Mendez, M Shoeybi, SK Lele, P Moin
International Journal of Aeroacoustics 12 (1-2), 1-20, 2013
Training question answering models from synthetic data
R Puri, R Spring, M Patwary, M Shoeybi, B Catanzaro
arXiv preprint arXiv:2002.09599, 2020
MEGATRON-CNTRL: Controllable story generation with external knowledge using large-scale language models
P Xu, M Patwary, M Shoeybi, R Puri, P Fung, A Anandkumar, B Catanzaro
arXiv preprint arXiv:2010.00840, 2020
BioMegatron: Larger biomedical domain language model
HC Shin, Y Zhang, E Bakhturina, R Puri, M Patwary, M Shoeybi, R Mani
arXiv preprint arXiv:2010.06060, 2020
Stable and accurate schemes for the compressible Navier–Stokes equations
K Mattsson, M Svärd, M Shoeybi
Journal of Computational Physics 227 (4), 2293-2316, 2008
Long-short transformer: Efficient transformers for language and vision
C Zhu, W Ping, C Xiao, M Shoeybi, T Goldstein, A Anandkumar, ...
Advances in neural information processing systems 34, 17723-17736, 2021
Unsupervised video interpolation using cycle consistency
FA Reda, D Sun, A Dundar, M Shoeybi, G Liu, KJ Shih, A Tao, J Kautz, ...
Proceedings of the IEEE/CVF international conference on computer Vision, 892-900, 2019
End-to-end training of neural retrievers for open-domain question answering
DS Sachan, M Patwary, M Shoeybi, N Kant, W Ping, WL Hamilton, ...
arXiv preprint arXiv:2101.00408, 2021
Reducing activation recomputation in large transformer models
VA Korthikanti, J Casper, S Lym, L McAfee, M Andersch, M Shoeybi, ...
Proceedings of Machine Learning and Systems 5, 2023
Factuality enhanced language models for open-ended text generation
N Lee, W Ping, P Xu, M Patwary, PN Fung, M Shoeybi, B Catanzaro
Advances in Neural Information Processing Systems 35, 34586-34599, 2022
FP8 formats for deep learning
P Micikevicius, D Stosic, N Burgess, M Cornea, P Dubey, R Grisenthwaite, ...
arXiv preprint arXiv:2209.05433, 2022
Numerical investigation of the acoustic behavior of a multi-perforated liner
J Eldredge, D Bodony, M Shoeybi
13th AIAA/CEAS Aeroacoustics Conference (28th AIAA Aeroacoustics Conference …, 2007
Large-eddy simulations of perfectly expanded supersonic jets using an unstructured solver
S Mendez, M Shoeybi, A Sharma, FE Ham, SK Lele, P Moin
AIAA journal 50 (5), 1103-1118, 2012
An adaptive implicit–explicit scheme for the DNS and LES of compressible flows on unstructured grids
M Shoeybi, M Svärd, FE Ham, P Moin
Journal of Computational Physics 229 (17), 5944-5965, 2010
Large-eddy simulations of perfectly-expanded supersonic jets: Quality assessment and validation
S Mendez, M Shoeybi, A Sharma, F Ham, S Lele, P Moin
48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and …, 2010
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