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Large-scale neural networks possess considerable expressive power.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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QSGD: Communication-efficient SGD via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J Dally · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Algorithmic management for improving collective productivity in crowdsourcing
Han Yu, Chunyan Miao, Yiqiang Chen, Simon Fauvel, Xiaoming Li, and Victor R. Lesser · 2017
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SignSGD: Compressed optimisation for non-convex problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar · 2018
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Analysis of quantized models
Lu Hou, Ruiliang Zhang, and James T Kwok · 2018
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Applied federated learning: Improving google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter, et al · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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FjORD: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horváth, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Lane · 2021
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Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, et al · 2021
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FedDrop: Trajectory-weighted dropout for efficient federated learning
Dongping Liao, Xitong Gao, Yiren Zhao, Hao Dai, Li Li, Kafeng Wang, Kejiang Ye, Yang Wang, and Cheng-Zhong Xu · 2021
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Fast adaptive active noise control based on modified model-agnostic meta-learning algorithm
Dongyuan Shi, Woon-Seng Gan, Bhan Lam, and Kenneth Ooi · 2021
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Dingzhu Wen, Ki-Jun Jeon, and Kaibin Huang · 2021
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FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization
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Adaptive federated dropout: Improving communication efficiency and generalization for federated learning
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Does federated dropout actually work?
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Securing federated learning: A covert communication-based approach
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Deep generative fixed-filter active noise control
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Transferable latent of cnn-based selective fixed-filter active noise control
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