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Transformers have emerged as the cornerstone of state-of-the-art natural language processing models, showcasing exceptional performance across a wide range of AI applications.
Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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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, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Online normalizer calculation for softmax
Maxim Milakov and Natalia Gimelshein · 2018
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov · 2019
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Openwebtext corpus, 2019
Aaron Gokaslan and Vanya Cohen · 2019
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Axial attention in multidimensional transformers
Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, and Tim Salimans · 2019
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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
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Compressive transformers for long-range sequence modelling
Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, and Timothy P Lillicrap · 2019
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Megatron-lm: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2019
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, et al · 2020
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Hippo: Recurrent memory with optimal polynomial projections
Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré · 2020
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Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya · 2020
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Gshard: 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 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He · 2020
Cited alongside, same era.
Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
Cited alongside, same era.
Lambdanetworks: Modeling long-range interactions without attention
Irwan Bello · 2021
Cited alongside, same era.
Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2021
Cited alongside, same era.
Transformer quality in linear time
Weizhe Hua, Zihang Dai, Hanxiao Liu, and Quoc Le · 2022
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Sparse upcycling: Training mixture-of-experts from dense checkpoints
Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, and Neil Houlsby · 2022
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Reducing activation recomputation in large transformer models
Vijay Korthikanti, Jared Casper, Sangkug Lym, Lawrence McAfee, Michael Andersch, Mohammad Shoeybi, and Bryan Catanzaro · 2022
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In-context reinforcement learning with algorithm distillation
Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, et al · 2022
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Competition-level code generation with alphacode
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Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira · 2021
Cited alongside, same era.
Urlb: Unsupervised reinforcement learning benchmark
Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, and Pieter Abbeel · 2021
Cited alongside, same era.
Colossal-ai: A unified deep learning system for large-scale parallel training
Shenggui Li, Jiarui Fang, Zhengda Bian, Hongxin Liu, Yuliang Liu, Haichen Huang, Boxiang Wang, and Yang You · 2021
Cited alongside, same era.
Do transformer modifications transfer across implementations and applications?
Sharan Narang, Hyung Won Chung, Yi Tay, William Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, et al · 2021
Cited alongside, same era.
Self-attention does not need o(n2) memory
Markus N Rabe and Charles Staats · 2021
Cited alongside, same era.
Msa transformer
Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, and Alexander Rives · 2021
Cited alongside, same era.
Alphafold and implications for intrinsically disordered proteins
Kiersten M Ruff and Rohit V Pappu · 2021
Cited alongside, same era.
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
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Mega: moving average equipped gated attention
Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer · 2022
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Junxiong Wang, Jing Nathan Yan, Albert Gu, and Alexander M Rush · 2022
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Don’t change the algorithm, change the data: Exploratory data for offline reinforcement learning
Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, and Lerrel Pinto · 2022
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Alpa: Automating inter-and { \{ Intra-Operator } \} parallelism for distributed deep learning
Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P Xing, et al · 2022
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Moebert: from bert to mixture-of-experts via importance-guided adaptation
Simiao Zuo, Qingru Zhang, Chen Liang, Pengcheng He, Tuo Zhao, and Weizhu Chen · 2022
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GitHub - karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs. — github.com
Kapathy Andrej · 2023
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Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou · 2023
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Fully Sharded Data Parallel: faster AI training with fewer GPUs — engineering.fb.com
Facebook · 2023
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Emergent agentic transformer from chain of hindsight experience
Hao Liu and Pieter Abbeel · 2023
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Chain of hindsight aligns language models with feedback
Hao Liu, Carlo Sferrazza, and Pieter Abbeel · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Resurrecting recurrent neural networks for long sequences
Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De · 2023
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Hyena hierarchy: Towards larger convolutional language models
Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, and Christopher Ré · 2023
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