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Densely connected convolutional networks
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Antonio Valerio Miceli Barone, Barry Haddow, Ulrich Germann, and Rico Sennrich · 2017
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Automatic differentiation in PyTorch
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Compressing bert: Studying the effects of weight pruning on transfer learning
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Measuring the algorithmic efficiency of neural networks, 2020
Danny Hernandez and Tom B. Brown · 2020
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Scaling laws for neural language models
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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Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, and Joseph E Gonzalez · 2020
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Comparing rewinding and fine-tuning in neural network pruning
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Green ai
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A neural scaling law from the dimension of the data manifold
Utkarsh Sharma and J. Kaplan · 2020
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The computational limits of deep learning, 2020
Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F. Manso · 2020
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Explaining neural scaling laws
Y. Bahri, Ethan Dyer, J. Kaplan, Jaehoon Lee, and Utkarsh Sharma · 2021
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On the predictability of pruning across scales
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Predicting accuracy on large datasets from smaller pilot data
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