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Training deep neural networks using a large batch size has shown promising results and benefits many real-world applications.
A method for solving the convex programming problem with convergence rate o (1/k
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Imagenet: A large-scale hierarchical image database
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Large scale distributed deep networks
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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On the convergence of block coordinate descent type methods
Amir Beck and Luba Tetruashvili · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Multi-gpu training of convnets
Omry Yadan, Keith Adams, Yaniv Taigman, and Marc’Aurelio Ranzato · 2013
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One weird trick for parallelizing convolutional neural networks
Alex Krizhevsky · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Asynchronous parallel stochastic gradient for nonconvex optimization
Xiangru Lian, Yijun Huang, Yuncheng Li, and Ji Liu · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2016
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Accelerated gradient methods for nonconvex nonlinear and stochastic programming
Saeed Ghadimi and Guanghui Lan · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 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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Scaling sgd batch size to 32k for imagenet training
Yang You, Igor Gitman, and Boris Ginsburg · 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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A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation
Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
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A comprehensive linear speedup analysis for asynchronous stochastic parallel optimization from zeroth-order to first-order
Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, and Ji Liu · 2016
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Unified convergence analysis of stochastic momentum methods for convex and non-convex optimization
Tianbao Yang, Qihang Lin, and Zhe Li · 2016
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Extremely large minibatch sgd: training resnet-50 on imagenet in 15 minutes
Takuya Akiba, Shuji Suzuki, and Keisuke Fukuda · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer, Itay Hubara, and Daniel Soudry · 2017
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Scaling Distributed Machine Learning with System and Algorithm Co-design
Mu Li · 2017
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Xianyan Jia, Shutao Song, Wei He, Yangzihao Wang, Haidong Rong, Feihu Zhou, Liqiang Xie, Zhenyu Guo, Yuanzhou Yang, Liwei Yu, et al · 2018
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Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
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Imagenet/resnet-50 training in 224 seconds
Hiroaki Mikami, Hisahiro Suganuma, Yoshiki Tanaka, Yuichi Kageyama, et al · 2018
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How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
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Measuring the effects of data parallelism on neural network training
Christopher J Shallue, Jaehoon Lee, Joe Antognini, Jascha Sohl-Dickstein, Roy Frostig, and George E Dahl · 2018
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Gradient diversity: a key ingredient for scalable distributed learning
Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, and Peter Bartlett · 2018
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Image classification at supercomputer scale
Chris Ying, Sameer Kumar, Dehao Chen, Tao Wang, and Youlong Cheng · 2018
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On lipschitz bounds of general convolutional neural networks
Dongmian Zou, Radu Balan, and Maneesh Singh · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Large-batch training for lstm and beyond
Yang You, Jonathan Hseu, Chris Ying, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh · 2019
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