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Sparsely-activated Mixture-of-experts (MoE) models allow the number of parameters to greatly increase while keeping the amount of computation for a given token or a given sample unchanged.
An iterative procedure for obtaining i-projections onto the intersection of convex sets
Richard L Dykstra · 1985
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A brief survey on power gating design
Ping Huang, Zuocheng Xing, Tianran Wang, Qiang Wei, Hongyan Wang, and Guitao Fu · 2010
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Exponentially increasing the capacity-to-computation ratio for conditional computation in deep learning, 2014
Kyunghyun Cho and Yoshua Bengio · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gaussian error linear units (GELUs), 2016
Dan Hendrycks and Kevin Gimpel · 2016
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Language modeling with gated convolutional networks
Yann N. Dauphin, Angela Fan, Michael Auli, and David Grangier · 2017
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Hard mixtures of experts for large scale weakly supervised vision, 2017
Sam Gross, Marc’Aurelio Ranzato, and Arthur Szlam · 2017
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Deep learning scaling is predictable, empirically, 2017
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov · 2019
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Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V. Le · 2019
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Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, and Zhifeng Chen · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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Megatron-lm: Training multi-billion parameter language models using model parallelism, 2020
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2020
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CoAtNet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc V. Le, and Mingxing Tan · 2021
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Glam: Efficient scaling of language models with mixture-of-experts, 2021
Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathy Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V Le, Yonghui Wu, Zhifeng Chen, and Claire Cui · 2021
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Tricks for training sparse translation models, 2021
Dheeru Dua, Shruti Bhosale, Vedanuj Goswami, James Cross, Mike Lewis, and Angela Fan · 2021
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Min Lin, Jie Fu, and Yoshua Bengio · 2019
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Pipedream: Generalized pipeline parallelism for dnn training
Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, and Matei Zaharia · 2019
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Green ai, 2019
Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni · 2019
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Conditional channel gated networks for task-aware continual learning
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A domain-specific supercomputer for training deep neural networks
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Scaling laws for neural language models, 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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In Advances in Neural Information Processing Systems
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, 2021
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GShard: Scaling giant models with conditional computation and automatic sharding
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Scalable transfer learning with expert models
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