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Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features

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Figure

Figure 1: Illustrations of the simple casecase (left), the gradient delay case (middle), and the gradient error (right) based on stochastic parallel learning
Figure 2: Experimental results on dense-CRF. For the 1st row, 1 to 10 denote the 10 threads of AsynGrad.For the 2nd row, AsynGrad denotes AsynGrad with 10 threads.
Table 2: Some major experimental results on LSTM. Time means time cost per iteration.POS-TagChunkingWeibo-WordSeg

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