Awesome-Mixture-of-Experts-Papers is a curated list of Mixture-of-Experts (MoE) papers in recent years. Star this repository, and then you can keep abreast of the latest developments of this booming research field.
Thanks to all the people who made contributions to this project. We strongly encourage the researchers to make pull request (e.g., add missing papers, fix errors) and help the others in this community!
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Taming Sparsely Activated Transformer with Stochastic Experts [pdf] ICLR 2022
Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan, Ruofei Zhang, Tuo Zhao, Jianfeng Gao
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Go Wider Instead of Deeper [pdf] AAAI 2022
Fuzhao Xue, Ziji Shi, Futao Wei, Yuxuan Lou, Yong Liu, Yang You
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StableMoE: Stable Routing Strategy for Mixture of Experts [pdf] [code] ACL 2022
Damai Dai, Li Dong, Shuming Ma, Bo Zheng, Zhifang Sui, Baobao Chang, Furu Wei
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Unified Scaling Laws for Routed Language Models [pdf] ICML 2022
Aidan Clark, Diego de las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew Johnson, Katie Millican, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Jack Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan
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Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs [pdf][code] NeurIPS 2022
Jinguo Zhu, Xizhou Zhu, Wenhai Wang, Xiaohua Wang, Hongsheng Li, Xiaogang Wang, Jifeng Dai
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A Theoretical View on Sparsely Activated Networks [pdf] [poster] ICML 2022 Workshop
Cenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang
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Designing Effective Sparse Expert Models [pdf] arXiv 2022
Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, William Fedus
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Mixture-of-Experts with Expert Choice Routing [pdf] arXiv 2022
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew Dai, Zhifeng Chen, Quoc Le, James Laudon
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Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models [pdf] arXiv 2022
Ze-Feng Gao, Peiyu Liu, Wayne Xin Zhao, Zhong-Yi Lu, Ji-Rong Wen
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Efficient Language Modeling with Sparse all-MLP [pdf] arXiv 2022
Ping Yu, Mikel Artetxe, Myle Ott, Sam Shleifer, Hongyu Gong, Ves Stoyanov, Xian Li
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One Student Knows All Experts Know: From Sparse to Dense[pdf] arXiv 2022
Fuzhao Xue, Xiaoxin He, Xiaozhe Ren, Yuxuan Lou, Yang You
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MoEC: Mixture of Expert Clusters[pdf] arXiv 2022
Yuan Xie, Shaohan Huang, Tianyu Chen, Furu Wei
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On the Representation Collapse of Sparse Mixture of Experts [pdf] arXiv 2022
Zewen Chi, Li Dong, Shaohan Huang, Damai Dai, Shuming Ma, Barun Patra, Saksham Singhal, Payal Bajaj, Xia Song, Furu Wei
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A Review of Sparse Expert Models in Deep Learning [pdf] arXiv 2022
William Fedus, Jeff Dean, Barret Zoph
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EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate [pdf]
Xiaonan Nie, Xupeng Miao, Shijie Cao, Lingxiao Ma, Qibin Liu, Jilong Xue, Youshan Miao, Yi Liu, Zhi Yang, Bin Cui
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muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems [pdf] arXiv 2022
Andrea Gesmundo, Jeff Dean
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Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts [pdf] arXiv 2022
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, Neil Houlsby
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ST-MoE: Designing Stable and Transferable Sparse Expert Models [pdf] arXiv 2022
Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, William Fedus
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Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity [pdf] arXiv 2021
William Fedus, Barret Zoph, Noam Shazeer
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GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding [pdf] ICLR 2021
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen
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GLaM: Efficient Scaling of Language Models with Mixture-of-Experts [pdf] arXiv 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, Claire Cui
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Scaling Vision with Sparse Mixture of Experts. [pdf] NeurIPS 2021
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby
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Scalable Transfer Learning with Expert Models [pdf] ICLR 2021
Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cedric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby
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Efficient Large Scale Language Modeling with Mixtures of Experts [pdf] arXiv 2021
Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giri Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Ves Stoyanov
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DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning [pdf] NeurIPS 2021
Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, Ed H. Chi
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Hash Layers For Large Sparse Models [pdf] NeurIPS 2021
Stephen Roller, Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston
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BASE Layers: Simplifying Training of Large, Sparse Models. [pdf] ICML 2021
Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer
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Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts [pdf] NeurIPS 2021 Workshop
Wouter Kool, Chris J. Maddison, Andriy Mnih
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M6-T: Exploring Sparse Expert Models and Beyond [pdf] arXiv 2021
An Yang, Junyang Lin, Rui Men, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Jiamang Wang, Yong Li, Di Zhang, Wei Lin, Lin Qu, Jingren Zhou, Hongxia Yang
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Dense-to-Sparse Gate for Mixture-of-Experts [pdf] arXiv 2021
Xiaonan Nie, Shijie Cao, Xupeng Miao, Lingxiao Ma, Jilong Xue, Youshan Miao, Zichao Yang, Zhi Yang, Bin Cui
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Sparse MoEs meet Efficient Ensembles [pdf] arXiv 2021
James Urquhart Allingham, Florian Wenzel, Zelda E Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton
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Towards More Effective and Economic Sparsely-Activated Model [pdf] arXiv 2021
Hao Jiang, Ke Zhan, Jianwei Qu, Yongkang Wu, Zhaoye Fei, Xinyu Zhang, Lei Chen, Zhicheng Dou, Xipeng Qiu, Zikai Guo, Ruofei Lai, Jiawen Wu, Enrui Hu, Yinxia Zhang, Yantao Jia, Fan Yu, Zhao Cao
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MoEfication: Conditional Computation of Transformer Models for Efficient Inference [pdf] arXiv 2021
Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
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Cross-token Modeling with Conditional Computation [pdf] arXiv 2021
Yuxuan Lou, Fuzhao Xue, Zangwei Zheng, Yang You
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Mixture Models for Diverse Machine Translation: Tricks of the Trade. [pdf] ICML 2019
Tianxiao Shen, Myle Ott, Michael Auli, Marc'Aurelio Ranzato
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Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. [pdf] ICLR 2017
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean
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FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models. [pdf] [code] PPoPP 2022
Jiaao He, Jidong Zhai, Tiago Antunes, Haojie Wang, Fuwen Luo, Shangfeng Shi, Qin Li
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DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale [pdf] arXiv 2022
Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He
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Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning [pdf] OSDI 2022
Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Joseph E. Gonzalez, Ion Stoica
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Pathways: Asynchronous Distributed Dataflow for ML [pdf] MLSys 2022
Paul Barham, Aakanksha Chowdhery, Jeff Dean, Sanjay Ghemawat, Steven Hand, Dan Hurt, Michael Isard, Hyeontaek Lim, Ruoming Pang, Sudip Roy, Brennan Saeta, Parker Schuh, Ryan Sepassi, Laurent El Shafey, Chandramohan A. Thekkath, Yonghui Wu
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HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System [pdf] arXiv 2022
Xiaonan Nie, Pinxue Zhao, Xupeng Miao, Tong Zhao, Bin Cui
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Tutel: Adaptive Mixture-of-Experts at Scale [pdf] arXiv 2022
Changho Hwang, Wei Cui, Yifan Xiong, Ziyue Yang, Ze Liu, Han Hu, Zilong Wang, Rafael Salas, Jithin Jose, Prabhat Ram, Joe Chau, Peng Cheng, Fan Yang, Mao Yang, Yongqiang Xiong
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FastMoE: A Fast Mixture-of-Expert Training System [pdf] arXiv 2021
Jiaao He, Jiezhong Qiu, Aohan Zeng, Zhilin Yang, Jidong Zhai, Jie Tang
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Learning Large-scale Universal User Representation with Sparse Mixture of Experts [pdf] ICML 2022
Caigao Jiang, Siqiao Xue, James Zhang, Lingyue Liu, Zhibo Zhu, Hongyan Hao
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Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic [pdf] SIGIR 2021
Dingcheng Li, Xu Li, Jun Wang, Ping Li
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Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference [pdf] EMNLP 2021
Sneha Kudugunta, Yanping Huang, Ankur Bapna, Maxim Krikun, Dmitry Lepikhin, Minh-Thang Luong, Orhan Firat
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MSSM: A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning [pdf] SIGIR 2021
Ke Ding, Xin Dong, Yong He, Lei Cheng, Chilin Fu, Zhaoxin Huan, Hai Li, Tan Yan, Liang Zhang, Xiaolu Zhang, Linjian Mo
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DEMix Layers: Disentangling Domains for Modular Language Modeling [pdf] arXiv 2021
Suchin Gururangan, Mike Lewis, Ari Holtzman, Noah A. Smith, Luke Zettlemoyer
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Multitask Mixture of Sequential Experts for User Activity Streams [pdf] SIGKDD 2020
Zhen Qin, Yicheng Cheng, Zhe Zhao, Zhe Chen, Donald Metzler, Jingzheng Qin
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Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations [pdf] RecSys 2020
Hongyan Tang, Junning Liu, Ming Zhao, Xudong Gong
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Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts [pdf] SIGKDD 2018
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, Ed Chi
- Tutel: An efficient mixture-of-experts implementation for large DNN model training [blog] [github] MSRA
- Deepspeed-MoE:Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale [github] Microsoft DeepSpeed
- FastMoE: A Fast Mixture-of-Expert Training System [github] Tsinghua University
- HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System [github] Peking University
Contributed by Xiaonan Nie, Xupeng Miao, Qibin Liu and Hetu team members.