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Rethinking Attention with Performers

Sep 30, 2020
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller

* 36 pages. This is an updated version of a previous submission which can be found at arXiv:2006.03555. See https://github.com/google-research/google-research/tree/master/protein_lm for protein language model code, and https://github.com/google-research/google-research/tree/master/performer for Performer code 

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Parallel Scheduled Sampling

Jun 11, 2019
Daniel Duckworth, Arvind Neelakantan, Ben Goodrich, Lukasz Kaiser, Samy Bengio

* Initial submission 

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Sample Efficient Text Summarization Using a Single Pre-Trained Transformer

May 21, 2019
Urvashi Khandelwal, Kevin Clark, Dan Jurafsky, Lukasz Kaiser


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Model-Based Reinforcement Learning for Atari

Mar 05, 2019
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Ryan Sepassi, George Tucker, Henryk Michalewski


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Area Attention

Oct 30, 2018
Yang Li, Lukasz Kaiser, Samy Bengio, Si Si


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Generating Wikipedia by Summarizing Long Sequences

Jan 30, 2018
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer

* Published as a conference paper at ICLR 2018 

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Unsupervised Cipher Cracking Using Discrete GANs

Jan 15, 2018
Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser


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Attention Is All You Need

Dec 06, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin

* 15 pages, 5 figures 

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One Model To Learn Them All

Jun 16, 2017
Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, Jakob Uszkoreit


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Depthwise Separable Convolutions for Neural Machine Translation

Jun 16, 2017
Lukasz Kaiser, Aidan N. Gomez, Francois Chollet


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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Mar 16, 2016
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

* Version 2 updates only the metadata, to correct the formatting of Mart\'in Abadi's name 

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Multi-task Sequence to Sequence Learning

Mar 01, 2016
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser

* 10 pages, 4 figures, ICLR 2016 camera-ready, added parsing SOTA results 

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Adding Gradient Noise Improves Learning for Very Deep Networks

Nov 21, 2015
Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens


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Grammar as a Foreign Language

Jun 09, 2015
Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton


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