Papers with stale gradient problem

1 papers
Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation (D18-1)

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Challenge: In order to achieve faster training we increase the mini-batch size and scale the learning rate accordingly.
Approach: They propose a technique that delays gradient updates by increasing the mini-batch size to improve the model's convergence.
Outcome: The proposed technique can train a shallow machine translation system 27% faster than an optimized baseline with negligible penalty in BLEU.

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