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Making Asynchronous Stochastic Gradient Descent Work for Transformers

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Figure

Table 1: Performance of the Transformer and RNNmodel trained synchronously and asynchronously,across different learning rates.
Table 2: The Adam optimizer slows down when gradients have larger variance even if they have the same average,in this case 1
Figure 1: The effect of batch sizes on convergence of Transformer and RNN models.
Table 5: Performance of the asynchronous Transformeron English to German with 4x Global accumulations(GA) across different learning rates on development setmeasured with BLEU score.

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