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Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019 · 1904
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Blockwise self-attention for long document understanding
Jiezhong Qiu, Hao Ma, Omer Levy, Scott Wen-tau Yih, Sinong Wang, and Jie Tang. 2019 · 1911
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Bp-transformer: Modelling long-range context via binary partitioning
Zihao Ye, Qipeng Guo, Quan Gan, Xipeng Qiu, and Zheng Zhang. 2019 · 1911
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How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2002
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Efficient content-based sparse attention with routing transformers
Aurko Roy, Mohammad Saffar, Ashish Vaswani, and David Grangier. 2020 · 2003
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2004
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2004
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Gmat: Global memory augmentation for transformers
Ankit Gupta and Jonathan Berant. 2020 · 2006
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, and Hao Ma. 2020 · 2006
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, A. Gane, Tamás Sarlós, P. Hawkins, J. Davis, Afroz Mohiuddin, L. Kaiser, D. Belanger, Lucy J. Colwell, and Adrian Weller. 2020 · 2009
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Notes about the carathéodory number
I. Bárány and R. Karasev. 2012 · 2012
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli. 2013 · 2013
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and R. Socher. 2017 · 2017
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A structural probe for finding syntax in word representations
John Hewitt and Christopher D Manning. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander Miller, and Sebastian Riedel. 2019 · 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 · 2019
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Transformer dissection: An unified understanding for transformer’s attention via the lens of kernel
Yao-Hung Hubert Tsai, Shaojie Bai, Makoto Yamada, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2019 · 2019
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. 2020 · 2020
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
A discourse-aware attention model for abstractive summarization of long documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, W. Chang, and Nazli Goharian. 2018 · 2018
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Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
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Deep contextualized word representations
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer. 2018 · 2018
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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 · 2019
Cited alongside, same era.
Sparse sinkhorn attention
Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, and Da-Cheng Juan. 2020a
Cited in the paper.
Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler. 2020b
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Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. 2020 · 2020
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Attention is not only a weight: Analyzing transformers with vector norms
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2020 · 2020
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Compressive transformers for long-range sequence modelling
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, and Timothy P. Lillicrap. 2020 · 2020
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Random feature attention
Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah Smith, and Lingpeng Kong. 2021 · 2021
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