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We propose a decentralized learning algorithm over a general social network.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Dual averaging for distributed optimization: Convergence analysis and network scaling
John C. Duchi, Alekh Agarwal, and Martin J. Wainwright · 2012
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Distributed alternating direction method of multipliers
E. Wei and A. Ozdaglar · 2012
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Machine Learning: A Probabilistic Perspective
Kevin P. Murphy · 2012
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Streaming variational bayes
Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C Wilson, and Michael I Jordan · 2013
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Non-asymptotic Convergence Rates for Cooperative Learning over Time-varying Directed Graphs
A. Nedić, A. Olshevsky, and C. A. Uribe · 2015
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Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
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Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Qualitatively characterizing neural network optimization problems
Ian Goodfellow, Oriol Vinyals, and Andrew Saxe · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Distributed Detection: Finite-Time Analysis and Impact of Network Topology
S. Shahrampour, A. Rakhlin, and A. Jadbabaie · 2016
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Uncertainty in Deep Learning
Yarin Gal · 2016
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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konecný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik · 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
Cited alongside, same era.
How to scale distributed deep learning?
Peter H. Jin, Qiaochu Yuan, Forrest N. Iandola, and Kurt Keutzer · 2016
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Social Learning and Distributed Hypothesis Testing
A. Lalitha, T. Javidi, and A. D. Sarwate · 2018
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Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2018
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CoCoA: A general framework for communication-efficient distributed optimization
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takáč, Michael I. Jordan, and Martin Jaggi · 2018
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Parle: parallelizing stochastic gradient descent
Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, and Ameet Talwalkar · 2017
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu · 2017
Cited alongside, same era.
Collaborative deep learning in fixed topology networks
Zhanhong Jiang, Aditya Balu, Chinmay Hegde, and Soumik Sarkar · 2017
Cited alongside, same era.
Tao Lin, Sebastian U. Stich, and Martin Jaggi · 2018
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d 2 d^{2} : Decentralized training over decentralized data
Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, and Ji Liu · 2018
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Optimization methods for large-scale machine learning
L. Bottou, F. Curtis, and J. Nocedal · 2018
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Jianyu Wang and Gauri Joshi · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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