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AI research is increasingly industry-driven, making it crucial to understand company contributions to this field.
On the stability of inverse problems
Andrey Nikolayevich Tikhonov et al · 1943
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Layer normalization, 2016
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Asynchronous methods for deep reinforcement learning, 2016
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Identity mappings in deep residual networks, 2016
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Convolutional sequence to sequence learning, 2017
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, 2017
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Generating wikipedia by summarizing long sequences, 2018
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer · 2018
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Don’t decay the learning rate, increase the batch size, 2018
Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, and Quoc V. Le · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost, 2018
Noam Shazeer and Mitchell Stern · 2018
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Mixed precision training, 2018
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu · 2018
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Generating long sequences with sparse transformers, 2019
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
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Fast transformer decoding: One write-head is all you need, 2019
Noam Shazeer · 2019
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Adaptive input representations for neural language modeling, 2019
Alexei Baevski and Michael Auli · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context, 2019
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension, 2019
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 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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The de-democratization of ai: Deep learning and the compute divide in artificial intelligence research, 2020
Nur Ahmed and Muntasir Wahed · 2020
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Language models are few-shot learners, 2020
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Scaling language models: Methods, analysis & insights from training gopher, 2022
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, and Geoffrey Irving · 2022
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Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Noam Shazeer · 2020
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Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tie-Yan Liu · 2020
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Ernie 3.0 titan: Exploring larger-scale knowledge enhanced pre-training for language understanding and generation, 2021
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Yuan 1.0: Large-scale pre-trained language model in zero-shot and few-shot learning, 2021
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Compute trends across three eras of machine learning, 2022
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Competition-level code generation with AlphaCode
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