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One of the biggest bottlenecks in a machine learning workflow is waiting for models to train.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms
James Bergstra, Dan Yamins, and David D Cox · 2013
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
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Are all training examples equally valuable?
Agata Lapedriza, Hamed Pirsiavash, Zoya Bylinskii, and Antonio Torralba · 2013
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
Cited alongside, same era.
Practical coreset constructions for machine learning
Olivier Bachem, Mario Lucic, and Andreas Krause · 2017
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
Cited alongside, same era.
Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret · 2018
Later among the works it cites.
Super-convergence: Very fast training of residual networks using large learning rates
Leslie N Smith and Nicholay Topin · 2018
Later among the works it cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
Later among the works it cites.
Imagenette
Jeremy Howard · 2019
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