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Mixture of Experts (MoE) models with conditional execution of sparsely activated layers have enabled training models with a much larger number of parameters.
Integer quantization for deep learning inference: Principles and empirical evaluation
Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev, and Paulius Micikevicius. 2020 · 2004
Earlier work this paper cites.
Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation
Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, and Noah A Smith. 2020 · 2006
Earlier work this paper cites.
Rethinking positional encoding in language pre-training
Guolin Ke, Di He, and Tie-Yan Liu. 2020 · 2006
Earlier work this paper cites.
Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen. 2020 · 2006
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
Earlier work this paper cites.
Mesh-tensorflow: Deep learning for supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, et al. 2018 · 2018
Cited alongside, same era.
From research to production and back: Ludicrously fast neural machine translation
Young Jin Kim, Marcin Junczys-Dowmunt, Hany Hassan, Alham Fikri Aji, Kenneth Heafield, Roman Grundkiewicz, and Nikolay Bogoychev. 2019 · 2019
Cited alongside, same era.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2021 · 2021
Cited alongside, same era.
Scalable and efficient moe training for multitask multilingual models
Young Jin Kim, Ammar Ahmad Awan, Alexandre Muzio, Andres Felipe Cruz Salinas, Liyang Lu, Amr Hendy, Samyam Rajbhandari, Yuxiong He, and Hany Hassan Awadalla. 2021 · 2021
Cited alongside, same era.
Gating dropout: Communication-efficient regularization for sparsely activated transformers
Rui Liu, Young Jin Kim, Alexandre Muzio, and Hany Hassan. 2022 · 2022
Closest in time.
Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale
Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, and Yuxiong He. 2022 · 2022
Closest in time.
Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model
Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, and Bryan Catanzaro. 2022 · 2022
Closest in time.
St-moe: Designing stable and transferable sparse expert models
Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, and William Fedus. 2022 · 2022
Closest in time.
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