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The remarkable progress in deep learning in recent years is largely driven by improvements in scale, where bigger models are trained on larger datasets for longer schedules.
Estimating dataset size requirements for classifying dna microarray data
Sayan Mukherjee, Pablo Tamayo, Simon Rogers, Ryan Rifkin, Anna Engle, Colin Campbell, Todd R Golub, and Jill P Mesirov · 2003
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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Predicting sample size required for classification performance
Rosa L Figueroa, Qing Zeng-Treitler, Sasikiran Kandula, and Long H Ngo · 2012
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Sample size planning for classification models
Claudia Beleites, Ute Neugebauer, Thomas Bocklitz, Christoph Krafft, and Jürgen Popp · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Junghwan Cho, Kyewook Lee, Ellie Shin, Garry Choy, and Synho Do · 2015
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Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Tobias Domhan, Jost Tobias Springenberg, and Frank Hutter · 2015
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Patwary, Mostofa Ali, Yang Yang, and Yanqi Zhou · 2017
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Learning curve prediction with bayesian neural networks
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, and Frank Hutter · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 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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Predicting accuracy on large datasets from smaller pilot data
Mark Johnson, Peter Anderson, Mark Dras, and Mark Steedman · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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A constructive prediction of the generalization error across scales
Jonathan S Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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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
Beyond the imitation game: Measuring and extrapolating the capabilities of language models
BIG-bench collaboration · 2021
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A theory of universal learning
Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon Van Handel, and Amir Yehudayoff · 2021
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Coatnet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc V Le, and Mingxing Tan · 2021
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Scaling laws for neural machine translation
Behrooz Ghorbani, Orhan Firat, Markus Freitag, Ankur Bapna, Maxim Krikun, Xavier Garcia, Ciprian Chelba, and Colin Cherry · 2021
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Data and parameter scaling laws for neural machine translation
Mitchell A Gordon, Kevin Duh, and Jared Kaplan · 2021
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Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Big transfer (BiT): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
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The deep bootstrap framework: Good online learners are good offline generalizers
Preetum Nakkiran, Behnam Neyshabur, and Hanie Sedghi · 2020
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Exploring the limits of large scale pre-training
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, and Hanie Sedghi · 2021
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Explaining neural scaling laws
Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, and Utkarsh Sharma · 2021
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Marcus Hutter · 2021
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Scaling laws for deep learning
Jonathan S Rosenfeld · 2021
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MLP-mixer: An all-MLP architecture for vision
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al · 2021
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Resnet strikes back: An improved training procedure in timm
Ross Wightman, Hugo Touvron, and Hervé Jégou · 2021
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X Zhai, A Kolesnikov, N Houlsby, and L Beyer · 2021
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Data scaling laws in NMT: The effect of noise and architecture
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Maxim Krikun, Colin Cherry, Behnam Neyshabur, and Orhan Firat · 2022
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Scaling laws from the data manifold dimension
Utkarsh Sharma and Jared Kaplan · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
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