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Scaling the size of language models usually leads to remarkable advancements in NLP tasks.
Adaptive mixtures of local experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton. 1991 · 1991
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Hierarchical mixtures of experts and the EM algorithm
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Omni-dimensional dynamic convolution
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Efficient large scale language modeling with mixtures of experts
Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giridharan Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O’Horo, Jeffrey Wang, Luke Zettlemoyer, Mona T. Diab, Zornitsa Kozareva, and Veselin Stoyanov. 2022 · 2022
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Glam: Efficient scaling of language models with mixture-of-experts
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 P. Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen S. Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, and Claire Cui. 2022 · 2022
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Parameter-efficient mixture-of-experts architecture for pre-trained language models
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Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020 · 2020
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Cheolhyoung Lee, Kyunghyun Cho, and Wanmo Kang. 2020 · 2020
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PAD-net: An efficient framework for dynamic networks
Shwai He, Liang Ding, Daize Dong, Boan Liu, Fuqiang Yu, and Dacheng Tao. 2023a
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Multimodal contrastive learning with limoe: the language-image mixture of experts
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Eliciting and understanding cross-task skills with task-level mixture-of-experts
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Uni-perceiver-moe: Learning sparse generalist models with conditional moes
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