Young Jin Kim
Young Jin Kim
Microsoft, Georgia Tech. (CSE)
Dirección de correo verificada de - Página principal
Citado por
Citado por
Prediction of weather-induced airline delays based on machine learning algorithms
S Choi, YJ Kim, S Briceno, D Mavris
Digital Avionics Systems Conference (DASC), 2016 IEEE/AIAA 35th, 2016
A deep learning approach to flight delay prediction
YJ Kim, S Choi, S Briceno, D Mavris
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), 2016
How good are gpt models at machine translation? a comprehensive evaluation
A Hendy, M Abdelrehim, A Sharaf, V Raunak, M Gabr, H Matsushita, ...
arXiv preprint arXiv:2302.09210, 2023
Lower numerical precision deep learning inference and training
A Rodriguez, E Segal, E Meiri, E Fomenko, YJ Kim, H Shen, B Ziv …, 2018
Taming sparsely activated transformer with stochastic experts
S Zuo, X Liu, J Jiao, YJ Kim, H Hassan, R Zhang, T Zhao, J Gao
arXiv preprint arXiv:2110.04260, 2021
From Research to Production and Back: Ludicrously Fast Neural Machine Translation
YJ Kim, M Junczys-Dowmunt, H Hassan, AF Aji, K Heafield, ...
Proceedings of the 3rd Workshop on Neural Generation and Translation, 280-288, 2019
Scalable and efficient moe training for multitask multilingual models
YJ Kim, AA Awan, A Muzio, AFC Salinas, L Lu, A Hendy, S Rajbhandari, ...
arXiv preprint arXiv:2109.10465, 2021
FastFormers: Highly efficient transformer models for natural language understanding
YJ Kim, HH Awadalla
arXiv preprint arXiv:2010.13382, 2020
Artificial neural network models for airport capacity prediction
S Choi, YJ Kim
Journal of Air Transport Management 97, 102146, 2021
Cost-sensitive prediction of airline delays using machine learning
S Choi, YJ Kim, S Briceno, D Mavris
2017 IEEE/AIAA 36th Digital Avionics Systems, 2017
A paradigm shift in machine translation: Boosting translation performance of large language models
H Xu, YJ Kim, A Sharaf, HH Awadalla
arXiv preprint arXiv:2309.11674, 2023
Gating dropout: Communication-efficient regularization for sparsely activated transformers
R Liu, YJ Kim, A Muzio, H Hassan
International Conference on Machine Learning, 13782-13792, 2022
Parallel Simulation of Agent-Based Model for Air Traffic Network
YJ Kim, OJ Pinon-Fischer, DN Mavris
AIAA Modeling and Simulation Technologies Conference, 2799, 2015
Time-and space-parallel simulation of air traffic networks
YJ Kim, D Mavris, R Fujimoto
Simulation 95 (12), 1213-1228, 2019
Finequant: Unlocking efficiency with fine-grained weight-only quantization for llms
YJ Kim, R Henry, R Fahim, HH Awadalla
arXiv preprint arXiv:2308.09723, 2023
Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production
YJ Kim, R Henry, R Fahim, HH Awadalla
arXiv preprint arXiv:2211.10017, 2022
Accelerating TensorFlow on Modern Intel Architectures
E Ould-Ahmed-Vall, M Abuzaina, MF Amin, J Bobba, RS Dubtsov, ...
International workshop on architectures for intelligent machines, 2017
Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness
YJ Kim, R Fahim, HH Awadalla
arXiv preprint arXiv:2310.02410, 2023
AutoMoE: Neural Architecture Search for Efficient Sparsely Activated Transformers
G Jawahar, S Mukherjee, X Liu, YJ Kim, M Abdul-Mageed, ...
arXiv preprint arXiv:2210.07535, 2022
A deep learning and parallel simulation methodology for air traffic management.
YJ Kim
Georgia Institute of Technology, Atlanta, GA, USA, 2018
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20