Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting

9 Dec 2015Abhimanu KumarPengtao XieJunming YinEric P. Xing

We propose a distributed approach to train deep neural networks (DNNs), which has guaranteed convergence theoretically and great scalability empirically: close to 6 times faster on instance of ImageNet data set when run with 6 machines. The proposed scheme is close to optimally scalable in terms of number of machines, and guaranteed to converge to the same optima as the undistributed setting... (read more)

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