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Sparse mixture of experts provides larger model capacity while requiring a constant computational overhead.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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Hierarchical mixtures of experts and the em algorithm
Michael I Jordan and Robert A Jacobs · 1994
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SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Conditional computation in neural networks for faster models
Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup · 2015
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Cross-lingual name tagging and linking for 282 languages
Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, and Heng Ji · 2017
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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
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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XNLI: Evaluating cross-lingual sentence representations
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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Cross-lingual language model pretraining
Alexis Conneau and Guillaume Lample · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Massively multilingual transfer for NER
Afshin Rahimi, Yuan Li, and Trevor Cohn · 2019
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CCNet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzman, Armand Joulin, and Edouard Grave · 2019
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PAWS-X: A cross-lingual adversarial dataset for paraphrase identification
Yinfei Yang, Yuan Zhang, Chris Tar, and Jason Baldridge · 2019
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Universal dependencies 2.5, 2019
Daniel Zeman, Joakim Nivre, Mitchell Abrams, and et al · 2019
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On the cross-lingual transferability of monolingual representations
Mikel Artetxe, Sebastian Ruder, and Dani Yogatama · 2020
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TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages
Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki · 2020
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov · 2020
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XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalization
Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson · 2020
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MLQA: Evaluating cross-lingual extractive question answering
Patrick Lewis, Barlas Oguz, Ruty Rinott, Sebastian Riedel, and Holger Schwenk · 2020
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2021
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Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition
Kenichi Kumatani, Robert Gmyr, Felipe Cruz Salinas, Linquan Liu, Wei Zuo, Devang Patel, Eric Sun, and Yu Shi · 2021
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{GS}hard: 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 · 2021
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Base layers: Simplifying training of large, sparse models
Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, and Luke Zettlemoyer · 2021
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Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
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Prevalence of neural collapse during the terminal phase of deep learning training
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Multi-task learning for multilingual neural machine translation
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Better fine-tuning by reducing representational collapse
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Efficient large scale language modeling with mixtures of experts
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Exploring simple siamese representation learning
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Hash layers for large sparse models
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A geometric analysis of neural collapse with unconstrained features
Zhihui Zhu, Tianyu DING, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, and Qing Qu · 2021
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BEiT: BERT pre-training of image transformers
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Unified scaling laws for routed language models
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Image as a foreign language: BEiT pretraining for all vision and vision-language tasks
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Mixture-of-experts with expert choice routing
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Designing effective sparse expert models
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